The AWS Certified Machine Learning Engineer – Associate certification demonstrates expertise in implementing ML workloads and operationalizing them in production. The AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam assesses a candidate’s skills in building, deploying, and maintaining machine learning (ML) solutions and pipelines using AWS Cloud. Further, the exam also tests the candidate’s ability to:

• Ingesting, transforming, validating, and preparing data for ML modeling.
• Selecting modeling techniques, training models, tuning hyperparameters, evaluating model performance, and managing model versions.
• Determining deployment infrastructure, provisioning compute resources, and configuring auto-scaling.
• Setting up CI/CD pipelines to automate ML workflow orchestration.
• Monitoring models, data, and infrastructure for issues.
• Securing ML systems and resources with access controls, compliance, and best practices.

Target Audience

The ideal candidate should have at least one year of experience working with Amazon SageMaker and other AWS services for ML engineering. Additionally, they should have at least one year of experience in a related role, such as a backend software developer, DevOps developer, data engineer, or data scientist.

Recommended General IT Knowledge

The ideal candidate should have the following IT knowledge:

• A basic understanding of common ML algorithms and their applications.
• Fundamentals of data engineering, including familiarity with data formats, ingestion, and transformation for ML data pipelines.
• Skills in querying and transforming data.
• Knowledge of software engineering best practices, such as modular code development, deployment, and debugging.
• Familiarity with provisioning and monitoring both cloud and on-premises ML resources.
• Experience with CI/CD pipelines and infrastructure as code (IaC).
• Proficiency in using code repositories for version control and CI/CD pipelines.

Recommended AWS Knowledge

The ideal candidate should have the following AWS expertise:

• Understanding of SageMaker’s capabilities and algorithms for building and deploying models.
• Knowledge of AWS data storage and processing services to prepare data for modeling.
• Experience with deploying applications and infrastructure on AWS.
• Familiarity with AWS monitoring tools for logging and troubleshooting ML systems.
• Knowledge of AWS services that facilitate automation and orchestration of CI/CD pipelines.
• Understanding of AWS security best practices, including identity and access management, encryption, and data protection.

Exam Details

The AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam is classified as an Associate-level certification. It has a duration of 170 minutes and includes 85 questions. Candidates can take the exam either at a Pearson VUE testing center or through an online proctored option. The exam is available in English and Japanese, with a minimum passing score of 720 on a scaled range of 100 to 1,000.

Question Types

The exam includes the following question formats:

• Multiple Choice: Contains one correct answer and three incorrect options (distractors).
• Multiple Response: Requires selecting two or more correct answers from five or more options. All correct responses must be chosen to earn credit.
• Ordering: Presents a list of 3-5 steps for completing a task. You must select and arrange the steps in the correct sequence.
• Matching: Involves matching a list of responses to 3-7 prompts. All pairs must be matched correctly to earn credit.
• Case Study: Features a single scenario with two or more related questions. Each question is evaluated individually, allowing candidates to earn credit for each correct answer.

Course Outline

This exam guide details the weightings, content domains, and task statements included in the exam. It offers further context for each task statement to support your preparation. The covered topics are:

Domain 1: Data Preparation for Machine Learning (ML)

Task Statement 1.1: Ingest and store data.

Knowledge of:

• Data formats and ingestion mechanisms (for example, validated and non-validated formats, Apache Parquet, JSON, CSV, Apache ORC, Apache Avro, RecordIO)
• How to use the core AWS data sources (for example, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon FSx for NetApp ONTAP)
• How to use AWS streaming data sources to ingest data (for example, Amazon Kinesis, Apache Flink, Apache Kafka)
• AWS storage options, including use cases and tradeoffs

Skills in:

• Extracting data from storage (for example, Amazon S3, Amazon Elastic Block Store [Amazon EBS], Amazon EFS, Amazon RDS, Amazon DynamoDB) by using relevant AWS service options (for example, Amazon S3 Transfer Acceleration, Amazon EBS Provisioned IOPS)
• Choosing appropriate data formats (for example, Parquet, JSON, CSV, ORC) based on data access patterns
• Ingesting data into Amazon SageMaker Data Wrangler and SageMaker Feature Store
• Merging data from multiple sources (for example, by using programming techniques, AWS Glue, Apache Spark)
• Troubleshooting and debugging data ingestion and storage issues that involve capacity and scalability
• Making initial storage decisions based on cost, performance, and data structure

Task Statement 1.2: Transform data and perform feature engineering.

Knowledge of:

• Data cleaning and transformation techniques (for example, detecting and treating outliers, imputing missing data, combining, deduplication)
• Feature engineering techniques (for example, data scaling and standardization, feature splitting, binning, log transformation, normalization)
• Encoding techniques (for example, one-hot encoding, binary encoding, label encoding, tokenization)
• Tools to explore, visualize, or transform data and features (for example, SageMaker Data Wrangler, AWS Glue, AWS Glue DataBrew)
• Services that transform streaming data (for example, AWS Lambda, Spark)
• Data annotation and labeling services that create high-quality labeled datasets

Skills in:

• Transforming data by using AWS tools (for example, AWS Glue, AWS Glue DataBrew, Spark running on Amazon EMR, SageMaker Data Wrangler)
• Creating and managing features by using AWS tools (for example, SageMaker Feature Store)
• Validating and labeling data by using AWS services (for example, SageMaker Ground Truth, Amazon Mechanical Turk)

Task Statement 1.3: Ensure data integrity and prepare data for modeling.

Knowledge of:

• Pre-training bias metrics for numeric, text, and image data (for example, class imbalance [CI], difference in proportions of labels [DPL])
• Strategies to address CI in numeric, text, and image datasets (for example, synthetic data generation, resampling)
• Techniques to encrypt data
• Data classification, anonymization, and masking
• Implications of compliance requirements (for example, personally identifiable information [PII], protected health information [PHI], data residency)

Skills in:

• Validating data quality (for example, by using AWS Glue DataBrew and AWS Glue Data Quality)
• Identifying and mitigating sources of bias in data (for example, selection bias, measurement bias) by using AWS tools (for example, SageMaker Clarify)
• Preparing data to reduce prediction bias (for example, by using dataset splitting, shuffling, and augmentation)
• Configuring data to load into the model training resource (for example, Amazon EFS, Amazon FSx)

Domain 2: ML Model Development

Task Statement 2.1: Choose a modeling approach.

Knowledge of:

• Capabilities and appropriate uses of ML algorithms to solve business problems
• How to use AWS artificial intelligence (AI) services (for example, Amazon Translate, Amazon Transcribe, Amazon Rekognition, Amazon Bedrock) to solve specific business problems
• How to consider interpretability during model selection or algorithm selection
• SageMaker built-in algorithms and when to apply them

Skills in:

• Assessing available data and problem complexity to determine the feasibility of an ML solution
• Comparing and selecting appropriate ML models or algorithms to solve specific problems
• Choosing built-in algorithms, foundation models, and solution templates (for example, in SageMaker JumpStart and Amazon Bedrock)
• Selecting models or algorithms based on costs
• Selecting AI services to solve common business needs

Task Statement 2.2: Train and refine models.

Knowledge of:

• Elements in the training process (for example, epoch, steps, batch size)
• Methods to reduce model training time (for example, early stopping, distributed training)
• Factors that influence model size
• Methods to improve model performance
• Benefits of regularization techniques (for example, dropout, weight decay, L1 and L2)
• Hyperparameter tuning techniques (for example, random search, Bayesian optimization)
• Model hyperparameters and their effects on model performance (for example, number of trees in a tree-based model, number of layers in a neural network)
• Methods to integrate models that were built outside SageMaker into SageMaker
  

Skills in:

• Using SageMaker built-in algorithms and common ML libraries to develop ML models
• Using SageMaker script mode with SageMaker supported frameworks to train models (for example, TensorFlow, PyTorch)
• Using custom datasets to fine-tune pre-trained models (for example, Amazon Bedrock, SageMaker JumpStart)
• Performing hyperparameter tuning (for example, by using SageMaker automatic model tuning [AMT])
• Integrating automated hyperparameter optimization capabilities
• Preventing model overfitting, underfitting, and catastrophic forgetting (for example, by using regularization techniques, feature selection)
• Combining multiple training models to improve performance (for example, ensembling, stacking, boosting)
• Reducing model size (for example, by altering data types, pruning, updating feature selection, compression)
• Managing model versions for repeatability and audits (for example, by using the SageMaker Model Registry)

Task Statement 2.3: Analyze model performance.

Knowledge of:

• Model evaluation techniques and metrics (for example, confusion matrix, heat maps, F1 score, accuracy, precision, recall, Root Mean Square Error [RMSE], receiver operating characteristic [ROC], Area Under the ROC Curve [AUC])
• Methods to create performance baselines
• Methods to identify model overfitting and underfitting
• Metrics available in SageMaker Clarify to gain insights into ML training data and models
• Convergence issues

Skills in:

• Selecting and interpreting evaluation metrics and detecting model bias
• Assessing tradeoffs between model performance, training time, and cost
• Performing reproducible experiments by using AWS services
• Comparing the performance of a shadow variant to the performance of a production variant
• Using SageMaker Clarify to interpret model outputs
• Using SageMaker Model Debugger to debug model convergence

Domain 3: Deployment and Orchestration of ML Workflows

Task Statement 3.1: Select deployment infrastructure based on existing architecture and requirements.

Knowledge of:

• Deployment best practices (for example, versioning, rollback strategies)
• AWS deployment services (for example, SageMaker)
• Methods to serve ML models in real time and in batches
• How to provision compute resources in production environments and test environments (for example, CPU, GPU)
• Model and endpoint requirements for deployment endpoints (for example, serverless endpoints, real-time endpoints, asynchronous endpoints, batch inference)
• How to choose appropriate containers (for example, provided or customized)
• Methods to optimize models on edge devices (for example, SageMaker Neo)

Skills in:

• Evaluating performance, cost, and latency tradeoffs
• Choosing the appropriate compute environment for training and inference based on requirements (for example, GPU or CPU specifications, processor family, networking bandwidth)
• Selecting the correct deployment orchestrator (for example, Apache Airflow, SageMaker Pipelines)
• Selecting multi-model or multi-container deployments
• Selecting the correct deployment target (for example, SageMaker endpoints, Kubernetes, Amazon Elastic Container Service [Amazon ECS], Amazon Elastic Kubernetes Service [Amazon EKS], Lambda)
• Choosing model deployment strategies (for example, real time, batch)
  

Task Statement 3.2: Create and script infrastructure based on existing architecture and requirements.

Knowledge of:

• Difference between on-demand and provisioned resources
• How to compare scaling policies
• Tradeoffs and use cases of infrastructure as code (IaC) options (for example, AWS CloudFormation, AWS Cloud Development Kit [AWS CDK])
• Containerization concepts and AWS container services
• How to use SageMaker endpoint auto scaling policies to meet scalability requirements (for example, based on demand, time)

Skills in:

• Applying best practices to enable maintainable, scalable, and cost-effective ML solutions (for example, automatic scaling on SageMaker endpoints, dynamically adding Spot Instances, by using Amazon EC2 instances, by using Lambda behind the endpoints)
• Automating the provisioning of compute resources, including communication between stacks (for example, by using CloudFormation, AWS CDK)
• Building and maintaining containers (for example, Amazon Elastic Container Registry [Amazon ECR], Amazon EKS, Amazon ECS, by using bring your own container [BYOC] with SageMaker)
• Configuring SageMaker endpoints within the VPC network
• Deploying and hosting models by using the SageMaker SDK
• Choosing specific metrics for auto scaling (for example, model latency, CPU utilization, invocations per instance)

Task Statement 3.3: Use automated orchestration tools to set up continuous integration and continuous delivery (CI/CD) pipelines.

Knowledge of:

• Capabilities and quotas for AWS CodePipeline, AWS CodeBuild, and AWS CodeDeploy
• Automation and integration of data ingestion with orchestration services
• Version control systems and basic usage (for example, Git)
• CI/CD principles and how they fit into ML workflows
• Deployment strategies and rollback actions (for example, blue/green, canary, linear)
• How code repositories and pipelines work together

Skills in:

• Configuring and troubleshooting CodeBuild, CodeDeploy, and CodePipeline, including stages
• Applying continuous deployment flow structures to invoke pipelines (for example, Gitflow, GitHub Flow)
• Using AWS services to automate orchestration (for example, to deploy ML models, automate model building)
• Configuring training and inference jobs (for example, by using Amazon EventBridge rules, SageMaker Pipelines, CodePipeline)
• Creating automated tests in CI/CD pipelines (for example, integration tests, unit tests, end-to-end tests)
• Building and integrating mechanisms to retrain models

Domain 4: ML Solution Monitoring, Maintenance, and Security

Task Statement 4.1: Monitor model inference.

Knowledge of:

• Drift in ML models
• Techniques to monitor data quality and model performance
• Design principles for ML lenses relevant to monitoring

Skills in:

• Monitoring models in production (for example, by using SageMaker Model Monitor)
• Monitoring workflows to detect anomalies or errors in data processing or model inference
• Detecting changes in the distribution of data that can affect model performance (for example, by using SageMaker Clarify)
• Monitoring model performance in production by using A/B testing

Task Statement 4.2: Monitor and optimize infrastructure and costs.

Knowledge of:

• Key performance metrics for ML infrastructure (for example, utilization, throughput, availability, scalability, fault tolerance)
• Monitoring and observability tools to troubleshoot latency and performance issues (for example, AWS X-Ray, Amazon CloudWatch Lambda Insights, Amazon CloudWatch Logs Insights)
• How to use AWS CloudTrail to log, monitor, and invoke re-training activities
• Differences between instance types and how they affect performance (for example, memory optimized, compute optimized, general purpose, inference optimized)
• Capabilities of cost analysis tools (for example, AWS Cost Explorer, AWS Billing and Cost Management, AWS Trusted Advisor)
• Cost tracking and allocation techniques (for example, resource tagging)

Skills in:

• Configuring and using tools to troubleshoot and analyze resources (for example, CloudWatch Logs, CloudWatch alarms)
• Creating CloudTrail trails
• Setting up dashboards to monitor performance metrics (for example, by using Amazon QuickSight, CloudWatch dashboards)
• Monitoring infrastructure (for example, by using EventBridge events)
• Rightsizing instance families and sizes (for example, by using SageMaker Inference Recommender and AWS Compute Optimizer)
• Monitoring and resolving latency and scaling issues
• Preparing infrastructure for cost monitoring (for example, by applying a tagging strategy)
• Troubleshooting capacity concerns that involve cost and performance (for example, provisioned concurrency, service quotas, auto scaling)
• Optimizing costs and setting cost quotas by using appropriate cost management tools (for example, AWS Cost Explorer, AWS Trusted Advisor, AWS Budgets)
• Optimizing infrastructure costs by selecting purchasing options (for example, Spot Instances, On-Demand Instances, Reserved Instances, SageMaker Savings Plans)

Task Statement 4.3: Secure AWS resources.

Knowledge of:

• IAM roles, policies, and groups that control access to AWS services (for example, AWS Identity and Access Management [IAM], bucket policies, SageMaker Role Manager)
• SageMaker security and compliance features
• Controls for network access to ML resources
• Security best practices for CI/CD pipelines

Skills in:

• Configuring least privilege access to ML artifacts
• Configuring IAM policies and roles for users and applications that interact with ML systems
• Monitoring, auditing, and logging ML systems to ensure continued security and compliance
• Troubleshooting and debugging security issues
• Building VPCs, subnets, and security groups to securely isolate ML systems

AWS Certified Machine Learning Engineer – Associate: FAQs

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AWS Exam Policy

Amazon Web Services (AWS) establishes clear rules and procedures for their certification exams. These guidelines address multiple facets of exam preparation and certification. Some of the key policies include:

Retake Policy

If you do not pass an exam, you must wait 14 calendar days before you can retake it. There is no limit on the number of attempts, but you will need to pay the full registration fee for each try. After passing an exam, you cannot retake the same exam for two years. However, if the exam has been updated with a new exam guide and exam series code, you will be eligible to take the updated version.

Exam Results

The AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam is designated as either pass or fail. Scoring is based on a minimum standard set by AWS professionals who adhere to certification industry best practices and guidelines. Your exam results are presented as a scaled score ranging from 100 to 1,000, with a minimum passing score of 720. This score reflects your overall performance on the exam and indicates whether you passed. Scaled scoring models are used to standardize scores across various exam forms that may vary in difficulty. Your score report may include a table that classifies your performance in each section. The exam employs a compensatory scoring model, meaning you do not need to achieve a passing score in every section; you only need to pass the overall exam.

AWS Certified Machine Learning Engineer – Associate Exam Study Guide

1. Understand the Exam Guide

Using the AWS Certified Machine Learning Engineer – Associate Exam guide is crucial for effective exam preparation. This guide provides a detailed overview of the exam structure, including the weightings for different content domains and specific task statements. By reviewing these sections, candidates can pinpoint key focus areas and adjust their study time accordingly.

Furthermore, the guide offers insights into the types of questions that may be included in the exam, allowing candidates to become familiar with the format and refine their test-taking strategies. Utilizing this resource can significantly improve your understanding of AI and machine learning concepts as they apply to AWS, ultimately increasing your confidence and readiness for the certification exam.

2. Use AWS Training Live on Twitch

Use free, live, and on-demand training through a dedicated Twitch channel. Interact with AWS experts during live broadcasts that cover a range of topics related to AWS services and solutions. These interactive sessions offer a unique chance to ask questions in real time and gain insights from industry professionals. Additionally, you can connect with a vibrant community of learners and AWS enthusiasts, exchanging knowledge and experiences. If you happen to miss a live session, our channel also provides a variety of on-demand training resources that you can access at your convenience.

3. EXAM PREP – AWS Certified Machine Learning Engineer – Associate

Receive guidance from the beginning to becoming an AWS Certified Machine Learning Engineer – Associate. Maximize your study time with AWS Skill Builder’s four-step exam preparation process, allowing for seamless learning whenever and wherever you need it. This exam validates your technical ability to implement and operationalize ML workloads in production. Enhance your career profile and credibility, positioning yourself for in-demand roles in the field of machine learning.

4. Join Study Groups

Joining study groups offers a dynamic and collaborative way to prepare for the AWS Certified Machine Learning Engineer – Associate exam. By participating in these groups, you connect with a community of individuals who are also navigating the complexities of AWS certifications. Engaging in discussions, sharing experiences, and addressing challenges together can provide valuable insights and deepen your understanding of key concepts.

Study groups create a supportive environment where members can clarify doubts, exchange tips, and stay motivated throughout their certification journey. This collaborative learning experience not only strengthens your grasp of AWS technologies but also fosters a sense of camaraderie among peers with similar goals.

5. Use Practice Tests

Incorporating practice tests into your study strategy for the AWS Certified Machine Learning Engineer – Associate exam is essential for success. These practice tests mimic the actual exam environment, allowing you to assess your knowledge, identify areas for improvement, and familiarize yourself with the types of questions you may encounter.

Regularly taking practice tests boosts your confidence, sharpens your time-management skills, and ensures you are well-prepared for the unique challenges of AWS certification exams. By blending the advantages of study groups with practice tests, you develop a comprehensive and effective approach to mastering AWS technologies and earning your certification.

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What is the AWS Certified Machine Learning Engineer – Associate Exam?

The AWS Certified Machine Learning Engineer – Associate certification demonstrates expertise in implementing ML workloads and operationalizing them in production. The AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam assesses a candidate’s skills in building, deploying, and maintaining machine learning (ML) solutions and pipelines using AWS Cloud. Further, the exam also tests the candidate’s ability to:

• Ingesting, transforming, validating, and preparing data for ML modeling.
• Selecting modeling techniques, training models, tuning hyperparameters, evaluating model performance, and managing model versions.
• Determining deployment infrastructure, provisioning compute resources, and configuring auto-scaling.
• Setting up CI/CD pipelines to automate ML workflow orchestration.
• Monitoring models, data, and infrastructure for issues.
• Securing ML systems and resources with access controls, compliance, and best practices.

What is the target audience for the AWS Certified Machine Learning Engineer – Associate Exam?

The ideal candidate should have at least one year of experience working with Amazon SageMaker and other AWS services for ML engineering. Additionally, they should have at least one year of experience in a related role, such as a backend software developer, DevOps developer, data engineer, or data scientist.

What is the knowledge requirement for the exam?

The ideal candidate should have the following IT knowledge:

• A basic understanding of common ML algorithms and their applications.
• Fundamentals of data engineering, including familiarity with data formats, ingestion, and transformation for ML data pipelines.
• Skills in querying and transforming data.
• Knowledge of software engineering best practices, such as modular code development, deployment, and debugging.
• Familiarity with provisioning and monitoring both cloud and on-premises ML resources.
• Experience with CI/CD pipelines and infrastructure as code (IaC).
• Proficiency in using code repositories for version control and CI/CD pipelines.

Is there any required AWS Knowledge for the exam?

The ideal candidate should have the following AWS expertise:

• Understanding of SageMaker’s capabilities and algorithms for building and deploying models.
• Knowledge of AWS data storage and processing services to prepare data for modeling.
• Experience with deploying applications and infrastructure on AWS.
• Familiarity with AWS monitoring tools for logging and troubleshooting ML systems.
• Knowledge of AWS services that facilitate automation and orchestration of CI/CD pipelines.
• Understanding of AWS security best practices, including identity and access management, encryption, and data protection.

What is the AWS Certified Machine Learning Engineer – Associate Exam time duration?

The time duration for the exam is 170 minutes.

How many questions will be there on the exam?

The exam consists of 85 questions.

Is there any language and passing score for the exam?

Candidates can choose to take the exam at a Pearson VUE testing center or opt for an online proctored format, with availability in English and Japanese. The minimum passing score for the exam is 720 (scaled score of 100–1,000).

What is the AWS Certified Machine Learning Engineer – Associate exam question format?

The exam includes the following question formats:

• Multiple Choice: Contains one correct answer and three incorrect options (distractors).
• Multiple Response: Requires selecting two or more correct answers from five or more options. All correct responses must be chosen to earn credit.
• Ordering: Presents a list of 3-5 steps for completing a task. You must select and arrange the steps in the correct sequence.
• Matching: Involves matching a list of responses to 3-7 prompts. All pairs must be matched correctly to earn credit.
• Case Study: Features a single scenario with two or more related questions. Each question is evaluated individually, allowing candidates to earn credit for each correct answer.

What are the major topics covered in the exam?

The topics are:

• Domain 1: Data Preparation for Machine Learning (ML) (28%)
• Domain 2: ML Model Development (26%)
• Domain 3: Deployment and Orchestration of ML Workflows (22%)
• Domain 4: ML Solution Monitoring, Maintenance, and Security (24%)

What is the Exam Retake Policy?

If you do not pass an exam, you must wait 14 calendar days before you can retake it. There is no limit on the number of attempts, but you will need to pay the full registration fee for each try. After passing an exam, you cannot retake the same exam for two years. However, if the exam has been updated with a new exam guide and exam series code, you will be eligible to take the updated version.

What is the process for registering for an AWS Certification exam?

To register for an exam, log in to aws.training and select “Certification” from the top navigation menu. Then, click on the “AWS Certification Account” button and choose “Schedule New Exam.” Locate the exam you want to take and click on the “Schedule at Pearson VUE” button. You will be directed to the scheduling page of the test delivery provider, where you can finalize your exam registration.

When can I expect to receive my exam results?

You can access your exam results, including those for beta exams, within 5 business days after completing your test. An email notification will be sent to you once your results are available in your AWS Certification Account, specifically under Exam History.

What benefits are available for AWS Certified individuals?

Beyond confirming your technical abilities, AWS Certification provides concrete advantages that allow you to highlight your accomplishments and enhance your AWS expertise further.

Check Here For More

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The AWS Certified AI Practitioner certification demonstrates your proficiency in essential artificial intelligence (AI), machine learning (ML), and generative AI concepts and applications. The AWS Certified AI Practitioner (AIF-C01) exam is designed for individuals who can effectively showcase their comprehensive understanding of AI/ML, generative AI technologies, and related AWS services and tools, regardless of their specific job role. Further. the exam assesses a candidate’s ability to:

• Grasp the fundamental concepts, methods, and strategies of AI, ML, and generative AI, particularly in the context of AWS.
• Appropriately utilize AI/ML and generative AI technologies to formulate relevant questions within their organization.
• Identify the suitable types of AI/ML technologies to address specific use cases.
• Utilize AI, ML, and generative AI technologies responsibly.

Target Audience

The ideal candidate should have up to six months of experience with AI/ML technologies on AWS. While they may use AI/ML solutions on AWS, they are not required to have built these solutions. Roles include:

• Business analyst
• IT support
• Marketing Professional
• Product or project manager
• Line-of-business or IT manager
• Sales professional

Recommended AWS Knowledge

The candidate should have the following AWS knowledge:

• Understanding of core AWS services (such as Amazon EC2, Amazon S3, AWS Lambda, and Amazon SageMaker) and their respective use cases.
• Awareness of the AWS shared responsibility model for security and compliance within the AWS Cloud.
• Familiarity with AWS Identity and Access Management (IAM) for securing and managing access to AWS resources.
• Knowledge of the AWS global infrastructure, including concepts related to AWS Regions, Availability Zones, and edge locations.
• Understanding of AWS service pricing models.

Exam Details

The AWS Certified AI Practitioner exam, categorized as foundational, lasts 120 minutes and consists of 85 questions. Candidates can choose to take the exam at a Pearson VUE testing center or opt for an online proctored format, with availability in English and Japanese. The minimum passing score for the exam is 700 (scaled score of 100–1,000).

Question Types

The exam includes one or more of the following types of questions:

• Multiple Choice: Contains one correct answer and three incorrect options (distractors).
• Multiple Response: Features two or more correct answers among five or more options. To earn credit, you must select all correct responses.
• Ordering: Provides a list of 3–5 responses that need to be arranged to complete a specific task. You must select the correct responses and arrange them in the proper order to receive credit.
• Matching: Involves a list of responses that must be matched with 3–7 prompts. You must correctly pair all options to earn credit.
• Case Study: Consists of a scenario followed by two or more questions related to it. The scenario remains the same for each question within the case study, and each question will be graded separately, allowing you to receive credit for each correctly answered question.

Course Outline

This exam guide outlines the weightings, content domains, and task statements associated with the exam. It provides additional context for each task statement to assist you in your preparation. The topics are:

Domain 1: Fundamentals of AI and ML

Task Statement 1.1: Explain basic AI concepts and terminologies.

Objectives:

• Define basic AI terms (for example, AI, ML, deep learning, neural networks, computer vision, natural language processing [NLP], model, algorithm, training and inferencing, bias, fairness, fit, large language model [LLM]).
• Describe the similarities and differences between AI, ML, and deep learning.
• Describe various types of inferencing (for example, batch, real-time).
• Describe the different types of data in AI models (for example, labeled and unlabeled, tabular, time-series, image, text, structured and unstructured).
• Describe supervised learning, unsupervised learning, and reinforcement learning.

Task Statement 1.2: Identify practical use cases for AI.

Objectives:

• Recognize applications where AI/ML can provide value (for example, assist human decision making, solution scalability, automation).
• Determine when AI/ML solutions are not appropriate (for example, costbenefit analyses, situations when a specific outcome is needed instead of a prediction).
• Select the appropriate ML techniques for specific use cases (for example, regression, classification, clustering).
• Identify examples of real-world AI applications (for example, computer vision, NLP, speech recognition, recommendation systems, fraud detection, forecasting).
• Explain the capabilities of AWS managed AI/ML services (for example, SageMaker, Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, Amazon Polly).

Task Statement 1.3: Describe the ML development lifecycle.

Objectives:

• Describe components of an ML pipeline (for example, data collection, exploratory data analysis [EDA], data pre-processing, feature engineering, model training, hyperparameter tuning, evaluation, deployment, monitoring).
• Understand sources of ML models (for example, open source pre-trained models, training custom models).
• Describe methods to use a model in production (for example, managed API service, self-hosted API).
• Identify relevant AWS services and features for each stage of an ML pipeline (for example, SageMaker, Amazon SageMaker Data Wrangler, Amazon SageMaker Feature Store, Amazon SageMaker Model Monitor).
• Understand fundamental concepts of ML operations (MLOps) (for example, experimentation, repeatable processes, scalable systems, managing technical debt, achieving production readiness, model monitoring, model re-training).
• Understand model performance metrics (for example, accuracy, Area Under the ROC Curve [AUC], F1 score) and business metrics (for example, cost per user, development costs, customer feedback, return on investment [ROI]) to evaluate ML models.

Domain 2: Fundamentals of Generative AI

Task Statement 2.1: Explain the basic concepts of generative AI.

Objectives:

• Understand foundational generative AI concepts (for example, tokens, chunking, embeddings, vectors, prompt engineering, transformer-based LLMs, foundation models, multi-modal models, diffusion models).
• Identify potential use cases for generative AI models (for example, image, video, and audio generation; summarization; chatbots; translation; code generation; customer service agents; search; recommendation engines).
• Describe the foundation model lifecycle (for example, data selection, model selection, pre-training, fine-tuning, evaluation, deployment, feedback).

Task Statement 2.2: Understand the capabilities and limitations of generative AI for solving business problems.

Objectives:

• Describe the advantages of generative AI (for example, adaptability, responsiveness, simplicity).
• Identify disadvantages of generative AI solutions (for example, hallucinations, interpretability, inaccuracy, nondeterminism).
• Understand various factors to select appropriate generative AI models (for example, model types, performance requirements, capabilities, constraints, compliance).
• Determine business value and metrics for generative AI applications (for example, cross-domain performance, efficiency, conversion rate, average revenue per user, accuracy, customer lifetime value).

Task Statement 2.3: Describe AWS infrastructure and technologies for building generative AI applications.

Objectives:

• Identify AWS services and features to develop generative AI applications (for example, Amazon SageMaker JumpStart; Amazon Bedrock; PartyRock, an Amazon Bedrock Playground; Amazon Q).
• Describe the advantages of using AWS generative AI services to build applications (for example, accessibility, lower barrier to entry, efficiency, cost-effectiveness, speed to market, ability to meet business objectives).
• Understand the benefits of AWS infrastructure for generative AI applications (for example, security, compliance, responsibility, safety).
• Understand cost tradeoffs of AWS generative AI services (for example, responsiveness, availability, redundancy, performance, regional coverage, token-based pricing, provision throughput, custom models).

Domain 3: Applications of Foundation Models

Task Statement 3.1: Describe design considerations for applications that use foundation models.

Objectives:

• Identify selection criteria to choose pre-trained models (for example, cost, modality, latency, multi-lingual, model size, model complexity, customization, input/output length).
• Understand the effect of inference parameters on model responses (for example, temperature, input/output length).
• Define Retrieval Augmented Generation (RAG) and describe its business applications (for example, Amazon Bedrock, knowledge base).
• Identify AWS services that help store embeddings within vector databases (for example, Amazon OpenSearch Service, Amazon Aurora, Amazon Neptune, Amazon DocumentDB [with MongoDB compatibility], Amazon RDS for PostgreSQL).
• Explain the cost tradeoffs of various approaches to foundation model customization (for example, pre-training, fine-tuning, in-context learning, RAG).
• Understand the role of agents in multi-step tasks (for example, Agents for Amazon Bedrock).

Task Statement 3.2: Choose effective prompt engineering techniques.

Objectives:

• Describe the concepts and constructs of prompt engineering (for example, context, instruction, negative prompts, model latent space).
• Understand techniques for prompt engineering (for example, chain-ofthought, zero-shot, single-shot, few-shot, prompt templates).
• Understand the benefits and best practices for prompt engineering (for example, response quality improvement, experimentation, guardrails, discovery, specificity and concision, using multiple comments).
• Define potential risks and limitations of prompt engineering (for example, exposure, poisoning, hijacking, jailbreaking).

Task Statement 3.3: Describe the training and fine-tuning process for foundation models.

Objectives:

• Describe the key elements of training a foundation model (for example, pre-training, fine-tuning, continuous pre-training).
• Define methods for fine-tuning a foundation model (for example, instruction tuning, adapting models for specific domains, transfer learning, continuous pre-training).
• Describe how to prepare data to fine-tune a foundation model (for example, data curation, governance, size, labeling, representativeness, reinforcement learning from human feedback [RLHF]).

Task Statement 3.4: Describe methods to evaluate foundation model performance.

Objectives:

• Understand approaches to evaluate foundation model performance (for example, human evaluation, benchmark datasets).
• Identify relevant metrics to assess foundation model performance (for example, Recall-Oriented Understudy for Gisting Evaluation [ROUGE], Bilingual Evaluation Understudy [BLEU], BERTScore).
• Determine whether a foundation model effectively meets business objectives (for example, productivity, user engagement, task engineering).

Domain 4: Guidelines for Responsible AI

Task Statement 4.1: Explain the development of AI systems that are responsible.

Objectives:

• Identify features of responsible AI (for example, bias, fairness, inclusivity, robustness, safety, veracity).
• Understand how to use tools to identify features of responsible AI (for example, Guardrails for Amazon Bedrock).
• Understand responsible practices to select a model (for example, environmental considerations, sustainability).
• Identify legal risks of working with generative AI (for example, intellectual property infringement claims, biased model outputs, loss of customer trust, end user risk, hallucinations).
• Identify characteristics of datasets (for example, inclusivity, diversity, curated data sources, balanced datasets).
• Understand effects of bias and variance (for example, effects on demographic groups, inaccuracy, overfitting, underfitting).
• Describe tools to detect and monitor bias, trustworthiness, and truthfulness (for example, analyzing label quality, human audits, subgroup analysis, Amazon SageMaker Clarify, SageMaker Model Monitor, Amazon Augmented AI [Amazon A2I]).

Task Statement 4.2: Recognize the importance of transparent and explainable models.

Objectives:

• Understand the differences between models that are transparent and explainable and models that are not transparent and explainable.
• Understand the tools to identify transparent and explainable models (for example, Amazon SageMaker Model Cards, open source models, data, licensing).
• Identify tradeoffs between model safety and transparency (for example, measure interpretability and performance).
• Understand principles of human-centered design for explainable AI.

Domain 5: Security, Compliance, and Governance for AI Solutions

Task Statement 5.1: Explain methods to secure AI systems.

Objectives:

• Identify AWS services and features to secure AI systems (for example, IAM roles, policies, and permissions; encryption; Amazon Macie; AWS PrivateLink; AWS shared responsibility model).
• Understand the concept of source citation and documenting data origins (for example, data lineage, data cataloging, SageMaker Model Cards).
• Describe best practices for secure data engineering (for example, assessing data quality, implementing privacy-enhancing technologies, data access control, data integrity).
• Understand security and privacy considerations for AI systems (for example, application security, threat detection, vulnerability management,
  infrastructure protection, prompt injection, encryption at rest and in transit).

Task Statement 5.2: Recognize governance and compliance regulations for AI systems.

Objectives:

• Identify regulatory compliance standards for AI systems (for example, International Organization for Standardization [ISO], System and Organization Controls [SOC], algorithm accountability laws).
• Identify AWS services and features to assist with governance and regulation compliance (for example, AWS Config, Amazon Inspector, AWS Audit Manager, AWS Artifact, AWS CloudTrail, AWS Trusted Advisor).
• Describe data governance strategies (for example, data lifecycles, logging, residency, monitoring, observation, retention).
• Describe processes to follow governance protocols (for example, policies, review cadence, review strategies, governance frameworks such as the Generative AI Security Scoping Matrix, transparency standards, team training requirements).

AWS Certified AI Practitioner: FAQs

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AWS Exam Policy

Amazon Web Services (AWS) establishes clear rules and procedures for their certification exams. These guidelines address multiple facets of exam preparation and certification. Some of the key policies include:

Retake Policy

If you do not pass an exam, you must wait 14 calendar days before you can retake it. There is no limit on the number of attempts, but you will need to pay the full registration fee for each try. After passing an exam, you cannot retake the same exam for two years. However, if the exam has been updated with a new exam guide and exam series code, you will be eligible to take the updated version.

Exam Results

The AWS Certified AI Practitioner (AIF-C01) exam is evaluated with a pass or fail designation. Scoring is based on a minimum standard set by AWS professionals adhering to certification industry best practices and guidelines. Your exam results are presented as a scaled score ranging from 100 to 1,000, with a minimum passing score of 700. This score reflects your overall performance on the exam and indicates whether you passed. Scaled scoring models ensure that scores are comparable across different exam forms that may vary slightly in difficulty.

AWS Certified AI Practitioner Exam Study Guide

1. Understand the Exam Guide

Utilizing the AWS Certified AI Practitioner exam guide is essential for effective exam preparation. This guide provides a comprehensive overview of the exam structure, including the weightings of different content domains and specific task statements. By reviewing these sections, candidates can identify key areas of focus and allocate their study time accordingly. Additionally, the guide offers insights into the types of questions that may appear on the exam, helping candidates familiarize themselves with the format and improve their test-taking strategies. Leveraging this resource can significantly enhance your understanding of AI and machine learning concepts as they relate to AWS, ultimately boosting your confidence and readiness for the certification exam.

2. Use AWS Training Live on Twitch

Experience free, live, and on-demand training through our dedicated Twitch channel. Engage with AWS experts during live broadcasts where they cover a variety of topics related to AWS services and solutions. These interactive sessions provide a unique opportunity to ask questions in real-time and gain insights from industry professionals. In addition to the live shows, you can connect with a vibrant community of learners and AWS enthusiasts, sharing knowledge and experiences. For those who may have missed a live session, our channel also offers a selection of on-demand training resources that you can access at your convenience.

3. EXAM PREP- AWS Certified AI Practitioner (AIF-C01)

Receive comprehensive guidance from the beginning of your journey to becoming an AWS Certified AI Practitioner. Maximize your study time with AWS Skill Builder’s four-step exam preparation process, designed for seamless learning whenever and wherever you need it. This exam certifies your knowledge of in-demand concepts and applications in artificial intelligence (AI), machine learning (ML), and generative AI.

4. Join Study Groups

Participating in study groups provides a dynamic and collaborative approach to preparing for the AWS Certified AI Practitioner exam. By joining these groups, you connect with a community of individuals who are also navigating the complexities of AWS certifications. Engaging in discussions, sharing experiences, and tackling challenges together can offer valuable insights and deepen your understanding of essential concepts. Study groups offers a supportive atmosphere where members can clarify doubts, exchange tips, and maintain motivation throughout their certification journey. This collaborative learning experience not only enhances your grasp of AWS technologies but also builds a sense of camaraderie among peers who share similar goals.

5. Use Practice Tests

Using practice tests for the AWS Certified AI Practitioner exam in your study strategy is crucial for exam success. These practice tests simulate the actual exam environment, enabling you to evaluate your knowledge, pinpoint areas for improvement, and become familiar with the types of questions you might encounter. Regularly taking practice tests helps build confidence, enhances your time-management skills, and ensures you are well-prepared for the specific challenges associated with AWS certification exams. By combining the benefits of study groups with practice tests, you create a comprehensive and effective approach to mastering AWS technologies and achieving your certification.

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What is the AWS Certified AI Practitioner Exam?

The AWS Certified AI Practitioner certification demonstrates your proficiency in essential artificial intelligence (AI), machine learning (ML), and generative AI concepts and applications. The AWS Certified AI Practitioner (AIF-C01) exam is designed for individuals who can effectively showcase their comprehensive understanding of AI/ML, generative AI technologies, and related AWS services and tools, regardless of their specific job role. Further. the exam assesses a candidate’s ability to:

• Grasp the fundamental concepts, methods, and strategies of AI, ML, and generative AI, particularly in the context of AWS.
• Appropriately utilize AI/ML and generative AI technologies to formulate relevant questions within their organization.
• Identify the suitable types of AI/ML technologies to address specific use cases.
• Utilize AI, ML, and generative AI technologies responsibly.

What is the target audience for the AWS Certified AI Practitioner Exam?

The ideal candidate should have up to six months of experience with AI/ML technologies on AWS. While they may use AI/ML solutions on AWS, they are not required to have built these solutions. Roles include:

• Business analyst
• IT support
• Marketing Professional
• Product or project manager
• Line-of-business or IT manager
• Sales professional

What is the knowledge requirement for the exam?

The candidate should have the following AWS knowledge:

• Understanding of core AWS services (such as Amazon EC2, Amazon S3, AWS Lambda, and Amazon SageMaker) and their respective use cases.
• Awareness of the AWS shared responsibility model for security and compliance within the AWS Cloud.
• Familiarity with AWS Identity and Access Management (IAM) for securing and managing access to AWS resources.
• Knowledge of the AWS global infrastructure, including concepts related to AWS Regions, Availability Zones, and edge locations.
• Understanding of AWS service pricing models.

What is the AWS Certified AI Practitioner Exam time duration?

The time duration for the exam is 120 minutes.

How many questions will be there on the exam?

The exam consists of 85 questions.

Is there any language and passing score for the exam?

Candidates can choose to take the exam at a Pearson VUE testing center or opt for an online proctored format, with availability in English and Japanese. The minimum passing score for the exam is 700 (scaled score of 100–1,000).

What is the AWS Certified AI Practitioner exam question format?

The exam includes one or more of the following types of questions:

• Multiple Choice: Contains one correct answer and three incorrect options (distractors).
• Multiple Response: Features two or more correct answers among five or more options. To earn credit, you must select all correct responses.
• Ordering: Provides a list of 3–5 responses that need to be arranged to complete a specific task. You must select the correct responses and arrange them in the proper order to receive credit.
• Matching: Involves a list of responses that must be matched with 3–7 prompts. You must correctly pair all options to earn credit.
• Case Study: Consists of a scenario followed by two or more questions related to it. The scenario remains the same for each question within the case study, and each question will be graded separately, allowing you to receive credit for each correctly answered question.

What are the major topics covered in the exam?

The topics are:

• Domain 1: Fundamentals of AI and ML (20%)
• Domain 2: Fundamentals of Generative AI (24%)
• Domain 3: Applications of Foundation Models (28%)
• Domain 4: Guidelines for Responsible AI (14%)
• Domain 5: Security, Compliance, and Governance for AI Solutions (14%)

What is the Exam Retake Policy?

If you do not pass an exam, you must wait 14 calendar days before you can retake it. There is no limit on the number of attempts, but you will need to pay the full registration fee for each try. After passing an exam, you cannot retake the same exam for two years. However, if the exam has been updated with a new exam guide and exam series code, you will be eligible to take the updated version.

What is the process for registering for an AWS Certification exam?

To register for an exam, log in to aws.training and select “Certification” from the top navigation menu. Then, click on the “AWS Certification Account” button and choose “Schedule New Exam.” Locate the exam you want to take and click on the “Schedule at Pearson VUE” button. You will be directed to the scheduling page of the test delivery provider, where you can finalize your exam registration.

When can I expect to receive my exam results?

You can access your exam results, including those for beta exams, within 5 business days after completing your test. An email notification will be sent to you once your results are available in your AWS Certification Account, specifically under Exam History.

What benefits are available for AWS Certified individuals?

Beyond confirming your technical abilities, AWS Certification provides concrete advantages that allow you to highlight your accomplishments and enhance your AWS expertise further.

Check Here For More

Go Back To The Tutorial

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The AWS Certified Data Engineer Associate (DEA-C01) exam confirms a candidate’s skill in setting up data pipelines and addressing issues related to cost and performance using best practices. The exam also verifies a candidate’s ability to:

• Ingest and transform data, and manage data pipelines with programming concepts.
• Opt for the best data store, devise data models, organize data schemas, and handle data lifecycles.
• Operate, sustain, and supervise data pipelines.
• Evaluate data and guarantee data quality.
• Implement suitable authentication, authorization, data encryption, privacy, and governance.
• Activate logging.

Target Audience

The ideal candidate should possess around 2–3 years of experience in data engineering. They should grasp how the volume, variety, and velocity of data impact aspects like ingestion, transformation, modeling, security, governance, privacy, schema design, and optimal data store design. Additionally, the candidate should have hands-on experience with AWS services for at least 1–2 years.

Recommended general IT knowledge includes:

• Setting up and maintaining extract, transform, and load (ETL) pipelines from ingestion to destination
• Application of high-level programming concepts, regardless of language, as required by the pipeline
• Utilization of Git commands for source control
• Knowledge of data lakes for storing data
• General understanding of networking, storage, and compute concepts

Recommended AWS knowledge for the candidate includes:

• Knowing how to utilize AWS services to complete the tasks outlined in the Introduction section of this exam guide
• Grasping the AWS services related to encryption, governance, protection, and logging for all data within data pipelines
• Being able to compare AWS services to comprehend the differences in cost, performance, and functionality
• Having the skill to structure and execute SQL queries on AWS services
• Understanding how to analyze data, check data quality, and maintain data consistency using AWS services

Exam Details

AWS Data Engineer Associate is an associate-level exam that will have 85 questions. The time duration for the exam is 170 minutes. The exam consists of two types of questions:

• Multiple choice: You choose one correct response from four options, including three incorrect ones (distractors).
• Multiple response: You pick two or more correct responses from five or more options.

The passing score for the exam is 720. The exam cost is 75$ USD and is available in English language.

Course Outline

This exam course ourline contains information about the weightings, content domains, and tasks for the exam. It provide extra details for each task statement to aid in your preparation. The exam is divided into different content domains, each with its own weighting.

Domain 1: Data Ingestion and Transformation

Task Statement 1.1: Perform data ingestion.

Knowledge of:

• Throughput and latency characteristics for AWS services that ingest data
• Data ingestion patterns (for example, frequency and data history) (AWS Documentation: Data ingestion patterns)
• Streaming data ingestion (AWS Documentation: Streaming ingestion)
• Batch data ingestion (for example, scheduled ingestion, event-driven ingestion) (AWS Documentation: Data ingestion methods)
• Replayability of data ingestion pipelines
• Stateful and stateless data transactions

Skills in:

• Reading data from streaming sources (for example, Amazon Kinesis, Amazon Managed Streaming for Apache Kafka [Amazon MSK], Amazon DynamoDB Streams, AWS Database Migration Service [AWS DMS], AWS Glue, Amazon Redshift) (AWS Documentation: Streaming ETL jobs in AWS Glue)
• Reading data from batch sources (for example, Amazon S3, AWS Glue, Amazon EMR, AWS DMS, Amazon Redshift, AWS Lambda, Amazon AppFlow) (AWS Documentation: Loading data from Amazon S3)
• Implementing appropriate configuration options for batch ingestion
• Consuming data APIs (AWS Documentation: Using the Amazon Redshift Data API)
• Setting up schedulers by using Amazon EventBridge, Apache Airflow, or time-based schedules for jobs and crawlers (AWS Documentation: Time-based schedules for jobs and crawlers)
• Setting up event triggers (for example, Amazon S3 Event Notifications, EventBridge) (AWS Documentation: Using EventBridge)
• Calling a Lambda function from Amazon Kinesis (AWS Documentation: Using Lambda with Kinesis Data Streams)
• Creating allowlists for IP addresses to allow connections to data sources (AWS Documentation: IP addresses to add to your allow list)
• Implementing throttling and overcoming rate limits (for example, DynamoDB, Amazon RDS, Kinesis) (AWS Documentation: Throttling issues for DynamoDB tables using provisioned capacity mode)
• Managing fan-in and fan-out for streaming data distribution (AWS Documentation: Developing Enhanced Fan-Out Consumers with the Kinesis Data Streams API)

Task Statement 1.2: Transform and process data.

Knowledge of:

• Creation of ETL pipelines based on business requirements (AWS Documentation: Build an ETL service pipeline)
• Volume, velocity, and variety of data (for example, structured data, unstructured data)
• Cloud computing and distributed computing (AWS Documentation: What is cloud computing?, What is Distributed Computing?)
• How to use Apache Spark to process data (AWS Documentation: Apache Spark)
• Intermediate data staging locations

Skills in:

• Optimizing container usage for performance needs (for example, Amazon Elastic Kubernetes Service [Amazon EKS], Amazon Elastic Container Service [Amazon ECS])
• Connecting to different data sources (for example, Java Database Connectivity [JDBC], Open Database Connectivity [ODBC]) (AWS Documentation: Connecting to Amazon Athena with ODBC and JDBC drivers)
• Integrating data from multiple sources (AWS Documentation: What is Data Integration?)
• Optimizing costs while processing data (AWS Documentation: Cost optimization)
• Implementing data transformation services based on requirements (for example, Amazon EMR, AWS Glue, Lambda, Amazon Redshift)
• Transforming data between formats (for example, from .csv to Apache Parquet) (AWS Documentation: Three AWS Glue ETL job types for converting data to Apache Parquet)
• Troubleshooting and debugging common transformation failures and performance issues (AWS Documentation: Troubleshooting resources)
• Creating data APIs to make data available to other systems by using AWS services (AWS Documentation: Using RDS Data API)

Task Statement 1.3: Orchestrate data pipelines.

Knowledge of:

• How to integrate various AWS services to create ETL pipelines
• Event-driven architecture (AWS Documentation: Event-driven architectures)
• How to configure AWS services for data pipelines based on schedules or dependencies (AWS Documentation: What is AWS Data Pipeline?)
• Serverless workflows

Skills in:

• Using orchestration services to build workflows for data ETL pipelines (for example, Lambda, EventBridge, Amazon Managed Workflows for Apache Airflow [Amazon MWAA], AWS Step Functions, AWS Glue workflows) (AWS Documentation: Migrating workloads from AWS Data Pipeline to Step Functions, Workflow orchestration)
• Building data pipelines for performance, availability, scalability, resiliency, and fault tolerance (AWS Documentation: Building a reliable data pipeline)
• Implementing and maintaining serverless workflows (AWS Documentation: Developing with a serverless workflow)
• Using notification services to send alerts (for example, Amazon Simple Notification Service [Amazon SNS], Amazon Simple Queue Service [Amazon SQS]) (AWS Documentation: Getting started with Amazon SNS)

Task Statement 1.4: Apply programming concepts.

Knowledge of:

• Continuous integration and continuous delivery (CI/CD) (implementation, testing, and deployment of data pipelines) (AWS Documentation: Continuous delivery and continuous integration)
• SQL queries (for data source queries and data transformations) (AWS Documentation: Using a SQL query to transform data)
• Infrastructure as code (IaC) for repeatable deployments (for example, AWS Cloud Development Kit [AWS CDK], AWS CloudFormation) (AWS Documentation: Infrastructure as code)
• Distributed computing (AWS Documentation: What is Distributed Computing?)
• Data structures and algorithms (for example, graph data structures and tree data structures)
• SQL query optimization

Skills in:

• Optimizing code to reduce runtime for data ingestion and transformation (AWS Documentation: Code optimization)
• Configuring Lambda functions to meet concurrency and performance needs (AWS Documentation: Understanding Lambda function scaling, Configuring reserved concurrency for a function)
• Performing SQL queries to transform data (for example, Amazon Redshift stored procedures) (AWS Documentation: Overview of stored procedures in Amazon Redshift)
• Structuring SQL queries to meet data pipeline requirements
• Using Git commands to perform actions such as creating, updating, cloning, and branching repositories (AWS Documentation: Basic Git commands)
• Using the AWS Serverless Application Model (AWS SAM) to package and deploy serverless data pipelines (for example, Lambda functions, Step Functions, DynamoDB tables) (AWS Documentation: What is the AWS Serverless Application Model (AWS SAM)?)
• Using and mounting storage volumes from within Lambda functions (AWS Documentation: Configuring file system access for Lambda functions)

Domain 2: Data Store Management

Task Statement 2.1: Choose a data store.

Knowledge of:

• Storage platforms and their characteristics (AWS Documentation: Storage)
• Storage services and configurations for specific performance demands
• Data storage formats (for example, .csv, .txt, Parquet) (AWS Documentation: Data format options for inputs and outputs in AWS Glue for Spark)
• How to align data storage with data migration requirements (AWS Documentation: AWS managed migration tools)
• How to determine the appropriate storage solution for specific access patterns (AWS Documentation: Choose the optimal storage based on access patterns, data growth, and the performance requirements)
• How to manage locks to prevent access to data (for example, Amazon Redshift, Amazon RDS) (AWS Documentation: LOCK)

Skills in:

• Implementing the appropriate storage services for specific cost and performance requirements (for example, Amazon Redshift, Amazon EMR, AWS Lake Formation, Amazon RDS, DynamoDB, Amazon Kinesis Data Streams, Amazon MSK) (AWS Documentation: Streaming ingestion)
• Configuring the appropriate storage services for specific access patterns and requirements (for example, Amazon Redshift, Amazon EMR, Lake Formation, Amazon RDS, DynamoDB) (AWS Documentation: What is AWS Lake Formation?, Querying external data using Amazon Redshift Spectrum)
• Applying storage services to appropriate use cases (for example, Amazon S3) (AWS Documentation: What is Amazon S3?)
• Integrating migration tools into data processing systems (for example, AWS Transfer Family)
• Implementing data migration or remote access methods (for example, Amazon Redshift federated queries, Amazon Redshift materialized views, Amazon Redshift Spectrum) (AWS Documentation: Querying data with federated queries in Amazon Redshift)

Task Statement 2.2: Understand data cataloging systems.

Knowledge of:

• How to create a data catalog (AWS Documentation: Getting started with the AWS Glue Data Catalog)
• Data classification based on requirements (AWS Documentation: Data classification models and schemes)
• Components of metadata and data catalogs (AWS Documentation: AWS Glue Data Catalog)

Skills in:

• Using data catalogs to consume data from the data’s source (AWS Documentation: Data discovery and cataloging in AWS Glue)
• Building and referencing a data catalog (for example, AWS Glue Data Catalog, Apache Hive metastore) (AWS Documentation: Using the AWS Glue Data Catalog as the metastore for Hive)
• Discovering schemas and using AWS Glue crawlers to populate data catalogs (AWS Documentation: Using crawlers to populate the Data Catalog)
• Synchronizing partitions with a data catalog (AWS Documentation: Best practices when using Athena with AWS Glue)
• Creating new source or target connections for cataloging (for example, AWS Glue) (AWS Documentation: Configuring data target nodes)

Task Statement 2.3: Manage the lifecycle of data.

Knowledge of:

• Appropriate storage solutions to address hot and cold data requirements (AWS Documentation: Cold storage for Amazon OpenSearch Service)
• How to optimize the cost of storage based on the data lifecycle (AWS Documentation: Storage optimization services)
• How to delete data to meet business and legal requirements
• Data retention policies and archiving strategies (AWS Documentation: Implement data retention policies for each class of data in the analytics workload)
• How to protect data with appropriate resiliency and availability (AWS Documentation: Data protection in AWS Resilience Hub)

Skills in:

• Performing load and unload operations to move data between Amazon S3 and Amazon Redshift (AWS Documentation: Unloading data to Amazon S3)
• Managing S3 Lifecycle policies to change the storage tier of S3 data (AWS Documentation: Managing your storage lifecycle)
• Expiring data when it reaches a specific age by using S3 Lifecycle policies (AWS Documentation: Expiring objects)
• Managing S3 versioning and DynamoDB TTL (AWS Documentation: Time to Live (TTL))

Task Statement 2.4: Design data models and schema evolution.

Knowledge of:

• Data modeling concepts (AWS Documentation: Data-modeling process steps)
• How to ensure accuracy and trustworthiness of data by using data lineage
• Best practices for indexing, partitioning strategies, compression, and other data optimization techniques (AWS Documentation: Optimize your data modeling and data storage for efficient data retrieval)
• How to model structured, semi-structured, and unstructured data (AWS Documentation: What’s The Difference Between Structured Data And Unstructured Data?)
• Schema evolution techniques (AWS Documentation: Handling schema updates)

Skills in:

• Designing schemas for Amazon Redshift, DynamoDB, and Lake Formation (AWS Documentation: CREATE SCHEMA)
• Addressing changes to the characteristics of data (AWS Documentation: Disaster recovery options in the cloud)
• Performing schema conversion (for example, by using the AWS Schema Conversion Tool [AWS SCT] and AWS DMS Schema Conversion) (AWS Documentation: Converting database schemas using DMS Schema Conversion)
• Establishing data lineage by using AWS tools (for example, Amazon SageMaker ML Lineage Tracking)

Domain 3: Data Operations and Support

Task Statement 3.1: Automate data processing by using AWS services.

Knowledge of:

• How to maintain and troubleshoot data processing for repeatable business outcomes (AWS Documentation: Define recovery objectives to maintain business continuity)
• API calls for data processing
• Which services accept scripting (for example, Amazon EMR, Amazon Redshift, AWS Glue) (AWS Documentation: What is AWS Glue?)

Skills in:

• Orchestrating data pipelines (for example, Amazon MWAA, Step Functions) (AWS Documentation: Workflow orchestration)
• Troubleshooting Amazon managed workflows (AWS Documentation: Troubleshooting Amazon Managed Workflows for Apache Airflow)
• Calling SDKs to access Amazon features from code (AWS Documentation: Code examples by SDK using AWS SDKs)
• Using the features of AWS services to process data (for example, Amazon EMR, Amazon Redshift, AWS Glue)
• Consuming and maintaining data APIs (AWS Documentation: API management)
• Preparing data transformation (for example, AWS Glue DataBrew) (AWS Documentation: What is AWS Glue DataBrew?)
• Querying data (for example, Amazon Athena)
• Using Lambda to automate data processing (AWS Documentation: AWS Lambda)
• Managing events and schedulers (for example, EventBridge) (AWS Documentation: What is Amazon EventBridge Scheduler?)

Task Statement 3.2: Analyze data by using AWS services.

Knowledge of:

• Tradeoffs between provisioned services and serverless services (AWS Documentation: Understanding serverless architectures)
• SQL queries (for example, SELECT statements with multiple qualifiers or JOIN clauses) (AWS Documentation: Subquery examples)
• How to visualize data for analysis (AWS Documentation: Analysis and visualization)
• When and how to apply cleansing techniques
• Data aggregation, rolling average, grouping, and pivoting (AWS Documentation: Aggregate functions, Using pivot tables)

Skills in:

• Visualizing data by using AWS services and tools (for example, AWS Glue DataBrew, Amazon QuickSight)
• Verifying and cleaning data (for example, Lambda, Athena, QuickSight, Jupyter Notebooks, Amazon SageMaker Data Wrangler)
• Using Athena to query data or to create views (AWS Documentation: Working with views)
• Using Athena notebooks that use Apache Spark to explore data (AWS Documentation: Using Apache Spark in Amazon Athena)

Task Statement 3.3: Maintain and monitor data pipelines.

Knowledge of:

• How to log application data (AWS Documentation: What is Amazon CloudWatch Logs?)
• Best practices for performance tuning (AWS Documentation: Best practices for performance tuning AWS Glue for Apache Spark jobs)
• How to log access to AWS services (AWS Documentation: Enabling logging from AWS services)
• Amazon Macie, AWS CloudTrail, and Amazon CloudWatch

Skills in:

• Extracting logs for audits (AWS Documentation: Logging and monitoring in AWS Audit Manager)
• Deploying logging and monitoring solutions to facilitate auditing and traceability (AWS Documentation: Designing and implementing logging and monitoring with Amazon CloudWatch)
• Using notifications during monitoring to send alerts
• Troubleshooting performance issues
• Using CloudTrail to track API calls (AWS Documentation: AWS CloudTrail)
• Troubleshooting and maintaining pipelines (for example, AWS Glue, Amazon EMR) (AWS Documentation: Building a reliable data pipeline)
• Using Amazon CloudWatch Logs to log application data (with a focus on configuration and automation)
• Analyzing logs with AWS services (for example, Athena, Amazon EMR, Amazon OpenSearch Service, CloudWatch Logs Insights, big data application logs) (AWS Documentation: Analyzing log data with CloudWatch Logs Insights)

Task Statement 3.4: Ensure data quality.

Knowledge of:

• Data sampling techniques (AWS Documentation: Using Spigot to sample your dataset)
• How to implement data skew mechanisms (AWS Documentation: Data skew)
• Data validation (data completeness, consistency, accuracy, and integrity)
• Data profiling

Skills in:

• Running data quality checks while processing the data (for example, checking for empty fields) (AWS Documentation: Data Quality Definition Language (DQDL) reference)
• Defining data quality rules (for example, AWS Glue DataBrew) (AWS Documentation: Validating data quality in AWS Glue DataBrew)
• Investigating data consistency (for example, AWS Glue DataBrew) (AWS Documentation: What is AWS Glue DataBrew)

Domain 4: Data Security and Governance

Task Statement 4.1: Apply authentication mechanisms.

Knowledge of:

• VPC security networking concepts (AWS Documentation: What is Amazon VPC?)
• Differences between managed services and unmanaged services
• Authentication methods (password-based, certificate-based, and role-based) (AWS Documentation: Authentication methods)
• Differences between AWS managed policies and customer managed policies (AWS Documentation: Managed policies and inline policies)

Skills in:

• Updating VPC security groups (AWS Documentation: Security group rules)
• Creating and updating IAM groups, roles, endpoints, and services (AWS Documentation: IAM Identities (users, user groups, and roles))
• Creating and rotating credentials for password management (for example, AWS Secrets Manager) (AWS Documentation: Password management with Amazon RDS and AWS Secrets Manager)
• Setting up IAM roles for access (for example, Lambda, Amazon API Gateway, AWS CLI, CloudFormation)
• Applying IAM policies to roles, endpoints, and services (for example, S3 Access Points, AWS PrivateLink) (AWS Documentation: Configuring IAM policies for using access points)

Task Statement 4.2: Apply authorization mechanisms.

Knowledge of:

• Authorization methods (role-based, policy-based, tag-based, and attributebased) (AWS Documentation: What is ABAC for AWS?)
• Principle of least privilege as it applies to AWS security
• Role-based access control and expected access patterns (AWS Documentation: Types of access control)
• Methods to protect data from unauthorized access across services (AWS Documentation: Mitigating Unauthorized Access to Data)

Skills in:

• Creating custom IAM policies when a managed policy does not meet the needs (AWS Documentation: Creating IAM policies (console))
• Storing application and database credentials (for example, Secrets Manager, AWS Systems Manager Parameter Store) (AWS Documentation: AWS Systems Manager Parameter Store)
• Providing database users, groups, and roles access and authority in a database (for example, for Amazon Redshift) (AWS Documentation: Example for controlling user and group access)
• Managing permissions through Lake Formation (for Amazon Redshift, Amazon EMR, Athena, and Amazon S3) (AWS Documentation: Managing Lake Formation permissions)

Task Statement 4.3: Ensure data encryption and masking.

Knowledge of:

• Data encryption options available in AWS analytics services (for example, Amazon Redshift, Amazon EMR, AWS Glue) (AWS Documentation: Data Encryption)
• Differences between client-side encryption and server-side encryption (AWS Documentation: Client-side and server-side encryption)
• Protection of sensitive data (AWS Documentation: Data protection in AWS Resource Groups)
• Data anonymization, masking, and key salting

Skills in:

• Applying data masking and anonymization according to compliance laws or company policies
• Using encryption keys to encrypt or decrypt data (for example, AWS Key Management Service [AWS KMS]) (AWS Documentation: Encrypting and decrypting data keys)
• Configuring encryption across AWS account boundaries (AWS Documentation: Allowing users in other accounts to use a KMS key)
• Enabling encryption in transit for data.

Task Statement 4.4: Prepare logs for audit.

Knowledge of:

• How to log application dat (AWS Documentation:a What is Amazon CloudWatch Logs?)
• How to log access to AWS services (AWS Documentation: Enabling logging from AWS services)
• Centralized AWS logs (AWS Documentation: Centralized Logging on AWS)

Skills in:

• Using CloudTrail to track API calls (AWS Documentation: AWS CloudTrail)
• Using CloudWatch Logs to store application logs (AWS Documentation: What is Amazon CloudWatch Logs?)
• Using AWS CloudTrail Lake for centralized logging queries (AWS Documentation: Querying AWS CloudTrail logs)
• Analyzing logs by using AWS services (for example, Athena, CloudWatch Logs Insights, Amazon OpenSearch Service) (AWS Documentation: Analyzing log data with CloudWatch Logs Insights)
• Integrating various AWS services to perform logging (for example, Amazon EMR in cases of large volumes of log data)

Task Statement 4.5: Understand data privacy and governance.Knowledge of:

• How to protect personally identifiable information (PII) (AWS Documentation: Personally identifiable information (PII))
• Data sovereignty

Skills in:

• Granting permissions for data sharing (for example, data sharing for Amazon Redshift) (AWS Documentation: Sharing data in Amazon Redshift)
• Implementing PII identification (for example, Macie with Lake Formation) (AWS Documentation: Data Protection in Lake Formation)
• Implementing data privacy strategies to prevent backups or replications of data to disallowed AWS Regions
• Managing configuration changes that have occurred in an account (for example, AWS Config) (AWS Documentation: Managing the Configuration Recorder)

AWS Data Engineer Associate Exam FAQs

Check here for FAQs!

AWS Exam Policy

Amazon Web Services (AWS) lays out specific rules and procedures for their certification exams. These guidelines cover various aspects of exam training and certification. Some of the key policies include:

Exam Retake Policy:

If a candidate doesn’t pass the exam, they must wait for 14 days before being eligible for a retake. There’s no limit on the number of attempts until the exam is passed, but the full registration fee is required for each attempt.

Exam Rescheduling:

To reschedule or cancel an exam, follow these steps:

1. Sign in to aws.training/Certification.
2. Click on the “Go to your Account” button.
3. Choose “Manage PSI” or “Pearson VUE Exams.”
4. You’ll be directed to the PSI or Pearson VUE dashboard.
5. If the exam is with PSI, click “View Details” for the scheduled exam. If it’s with Pearson VUE, select the exam in the “Upcoming Appointments” menu.
6. Keep in mind that you can reschedule the exam up to 24 hours before the scheduled time, and each appointment can only be rescheduled twice. If you need to take the exam a third time, you must cancel it and then schedule it for a suitable date.

AWS Data Engineer Associate Exam Study Guide

AWS Exam Page

AWS furnishes an exam page that includes the certification’s course outline, an overview, and crucial details. These information are crafted by AWS experts to showcase skills and guide candidates through hands-on exercises reflective of exam scenarios. Further, use the certification page validates proficiency in core data-related AWS services, the ability to implement data pipelines, troubleshoot issues, and optimize cost and performance following best practices. If you’re keen on leveraging AWS technology to transform data for analysis and actionable insights, taking this exam provides an early chance to earn the new certification.

AWS Learning Resources

AWS offers a diverse range of learning resources to cater to individuals at various stages of their cloud computing journey. From beginners seeking foundational knowledge to experienced professionals aiming to refine their skills, AWS provides comprehensive documentation, tutorials, and hands-on labs. The AWS Training and Certification platform offers structured courses led by expert instructors, covering a wide array of topics from cloud fundamentals to specialized domains like machine learning and security. Some of them for AWS Data Engineer Associate exams are:

• Engage in the best of re:Invent Analytics 2022 
• A Day in the Life of a Data Engineer 
• Building Batch Data Analytics Solutions on AWS 

Join Study Groups

Study groups offer a dynamic and collaborative approach to AWS exam preparation. By joining these groups, you gain access to a community of like-minded individuals who are also navigating the complexities of AWS certifications. Engaging in discussions, sharing experiences, and collectively tackling challenges can provide valuable insights and enhance your understanding of key concepts. Study groups create a supportive environment where members can clarify doubts, exchange tips, and stay motivated throughout their certification journey. This collaborative learning experience not only strengthens your grasp of AWS technologies but also fosters a sense of camaraderie among peers pursuing similar goals.

Use Practice Tests

Incorporating AWS practice tests into your preparation strategy is essential for achieving exam success. These practice tests simulate the actual exam environment, allowing you to assess your knowledge, identify areas for improvement, and familiarize yourself with the types of questions you may encounter. Regularly taking practice tests helps build confidence, refines your time-management skills, and ensures you are well-prepared for the specific challenges posed by AWS certification exams. The combination of study groups and practice tests creates a well-rounded and effective approach to mastering AWS technologies and earning your certification.

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What is AWS Certified Data Engineer Associate Exam?

The AWS Certified Data Engineer Associate (DEA-C01) exam confirms a candidate’s skill in setting up data pipelines and addressing issues related to cost and performance using best practices. The exam also verifies a candidate’s ability to:

• Ingest and transform data, and manage data pipelines with programming concepts.
• Opt for the best data store, devise data models, organize data schemas, and handle data lifecycles.
• Operate, sustain, and supervise data pipelines.
• Evaluate data and guarantee data quality.
• Implement suitable authentication, authorization, data encryption, privacy, and governance.
• Activate logging.

What is the knowledge requirment for AWS Data Engineer Associate Exam?

The ideal candidate should possess around 2–3 years of experience in data engineering. They should grasp how the volume, variety, and velocity of data impact aspects like ingestion, transformation, modeling, security, governance, privacy, schema design, and optimal data store design. Additionally, the candidate should have hands-on experience with AWS services for at least 1–2 years.

What is the course outline for AWS Data Engineer Associate Exam??

The main areas to focus on the exam are:

• Data Ingestion and Transformation 34%
• Data Store Management 26%
• Data Operations and Support 22%
• Data Security and Governance 18%

What is the time duration for the AWS Certified Data Engineer Associate Exam?

You will get 170 minutes to complete exam.

What are the languages available for AWS Certified Data Engineer Associate Exam?

This exam is available in English language.

Why should I consider AWS Certified? 

AWS Certification helps learners build credibility and confidence by validating their cloud expertise with an industry-recognized credential and organizations identify skilled professionals to lead cloud initiatives using AWS.

What is the retake policy

If you are unable to pass the exam, then you must wait 14 days before becoming eligible to retake the exam. Until you pass the test, there is no limit to the number of exam attempts. But, for each re-attempt, you must pay the full registration price fee. Also, the Beta exam test-takers will get one attempt only.

When will I get my result

Right after completing your exam, a pass or fail notification will be displayed on the testing screen. Also, candidates will be sent an email confirming their exam completion. A detailed exam result within five business days of completing your exam. This exam detail will appear on the Certification Account of yours, under Previous Exams.

What are the service and features covered in the exam?

They do not publish the services and features which are covered in its certification exam. The current topic areas and objectives covered in the exam, are given the exam guide, for reference.

Are there benefits offered to AWS certified Individuals

AWS offers several benefits to its certified members, apart from validating there skills. See the AWS Certification Benefits page to get a complete list of benefits.

Which certification program are available to take from home or office with online protoring

AWS offers its certification exams via online proctoring as well. AWS uses Pearson VUE, a third-party test delivery provider for its online proctoring exams. Visit the Pearson VUE site, to learn more about AWS online proctored certification exams.

How do I become AWS certified?

In order to become AWS certified, you must get a passing score in the proctored exam, and attain your Certification. After getting a passing score, they will send you your certification credentials. 

How long will be certification be valid?

AWS certified individuals should get their certification recertified, every three years. See the AWS Certification Recertification page for more details.

What is the difference between AWS Certification and Exam?

AWS exam refers to a test that is used to validate your technical knowledge of AWS products and services. On the other hand, AWS certification is a credential that you earn upon successfully passing exam. You are given a digital badge and title which can be used on business cards and other professional collateral to designate yourself as AWS Certified.

How often are exams updated?

AWS rotates its questions in and out, on a regular basis. This is done in adherence to the exam guide. The major revisions to an exam will be made public by AWS, via the Exam guide.

When AWS releases a new product or service, How soon will it appear on the exam?

Any new product, service, or feature will generally be made available, 6 months prior to it appearing on a certification exam.

If an existing feature or service has changed. How will that be reflected in the exam?

The AWS certification team will be replacing the exam questions, which are determined to be impacted by any change. 

How should I answer a question that I think has been affected by a change in service or product?

You must choose the best available answer from the given options in the question. 

What is the benefit of AWS certification digital badges?

AWS Certification offers digital badges to benefit you with increased earning as well as showcase your Certification status. they provide digital badges through Credly’s Acclaim platform to offer flexible options for recognition and verification. Also, you can benefit from one-click badge sharing on social media newsfeeds, tools for embedding verifiable badges on websites or email signatures.

I cannot find my digital badge on Credly’s Acclaim platform?

In case your digital badge(s) does not appear on Credly’s Acclaim platform, then you might have more than one AWS Certification Account. Ensure you are logged into the account that holds your required certification(s). If you have more than one AWS Certification Account with the same email address, you will need your accounts merged before you claim your badge(s) on Credly’s Acclaim platform.

Suggest the process to get a group of people certified AWS Professional.

In this case, you can purchase Certification exam vouchers, to eliminate the need for candidates to have to pay when scheduling their exam. They simply enter a voucher code when scheduling exams at either Pearson VUE or PSI.

What are the various ways to take the certification exam?

Certification exams are offered via online proctoring using the third-party test delivery provider Pearson VUE. Details of online proctoring are specified on Pearson VUE site. Pearson VUE handles your information in accordance with their privacy policies, posted on the Pearson VUE site. Providing Pearson VUE with your information may involve transferring it to another country.

Does AWS offer practice test for Certification?

Yes, they offers practice exams for all Foundational, Associate, and Professional Exams, as well as most of our Specialty exams. The practice exams will allow you to test your knowledge online in a timed environment, and experience the exam format and platform prior to taking the full exam. Practice exams can be purchased from our exam deliver providers through your Certification Account. The Foundational and Associate-level practice exams are 20 USD and the Professional and Specialty practice exams are 40 USD. Purchase of a practice exam provides you with one attempt. 

For how long is the practice exam available for Certification?

The access for the practice exam will expires after 180 days. Also after the practice exam is launched. you will have 30 days to complete the exam, or until the allotted practice exam time expires. Further, you have the option to pause your practice exam by closing out your exam browser. Selecting “End Test” will mark the exam complete and it cannot be restarted. 

How will get my score for practice exam?

On completing the practice exam, a score report will be emailed to you with high-level feedback to help you understand how you scored on the exam content covered on the practice exam. Please note, answers to the practice exam are not provided to the test taker. The exam guide is also provided with the score report to help you with your exam preparation. 

What is the process to arrange a special accommodation for the exam?

The Special accommodations will be arranged for you with the test delivery provider before you register for the exam. Please note, PSI & Pearson VUE do not share accommodation request details, so the appropriate documentation will need to be provided to the test delivery provider you wish to test with.

How to find the test centers near me?

You can find test centers with the following options –

• PSI test centers
• Pearson VUE test centers

For More Check AWS Exam Policies

Go back to Tutorials

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Cloud computing services are housed under AWS Certified Solutions Architect – Associate (SAA-C03), opening up a plethora of lucrative career opportunities. AWS offers more than 70 services, including those for compute, networking, databases, storage, analytics, application services, management, mobile deployment, developer tools, and the Internet of things. AWS furthermore provides cloud certifications that attest to your capability to work in the cloud. Each of the several certifications that AWS offers at various levels unlocks a wide range of improved employment options. You can choose to participate in either of the two certification paths recommended by AWS, depending on your interests and professional objectives.

The entry-level AWS Certified Solutions Architect Associate exam was created with people looking to enter this line of work in mind.

Candidates who are qualified to perform the duties of a solutions architect should take the AWS Certified Solutions Architect Associate exam. They must have at least one year of practical experience building fault-tolerant, scalable, cost-effective, and available distributed AWS systems. This test confirms a candidate’s ability to use Amazon Web Services technology to create and deliver secure and reliable applications. The article provides a list of AWS Certified Solutions Architect – Associate (SAA-C03) Sample Questions that cover core exam topics including –

• Module 1: Overview of Design Secure Architectures
• Module 2: Overview of Design Resilient Architectures
• Module 3: Overview of Design High-Performing Architectures
• Module 4: Overview of Design Cost-Optimized Architectures

Which AWS service can be used to store and retrieve any amount of data at any time, from anywhere on the web?

• a. Amazon Elastic Compute Cloud (EC2)
• b. Amazon Simple Storage Service (S3)
• c. Amazon Relational Database Service (RDS)
• d. Amazon DynamoDB

Answer: b. Amazon Simple Storage Service (S3)

Explanation: Amazon S3 is a highly scalable, durable, and secure object storage service that can store and retrieve any amount of data from anywhere on the web. Amazon EC2 is a web service that provides resizable compute capacity in the cloud. Amazon RDS is a web service that makes it easy to set up, operate, and scale a relational database in the cloud. Amazon DynamoDB is a fully managed NoSQL database service.

Which AWS service can be used to monitor and manage your AWS resources and applications?

• a. Amazon CloudFront
• b. Amazon CloudFormation
• c. Amazon CloudWatch
• d. Amazon Elastic Load Balancer

Answer: c. Amazon CloudWatch

Explanation: Amazon CloudWatch is a monitoring service that provides data and actionable insights for AWS resources and applications. Amazon CloudFront is a global content delivery network (CDN) service. Amazon CloudFormation is a service that allows you to model and provision AWS resources. Amazon Elastic Load Balancer is a service that automatically distributes incoming application traffic across multiple targets, such as EC2 instances.

Which of the following is a best practice for security in AWS?

• a. Using long, complex passwords that are difficult to remember.
• b. Granting permissions to users and applications on a need-to-know basis.
• c. Storing encryption keys in plain text. d. Disabling multi-factor authentication.

Answer: b. Granting permissions to users and applications on a need-to-know basis.

Explanation: Granting permissions to users and applications on a need-to-know basis is a best practice for security in AWS. It is also recommended to use multi-factor authentication, strong and unique passwords, and to encrypt sensitive data. Storing encryption keys in plain text is not recommended, as it makes them vulnerable to theft or misuse.

Which AWS service can be used to automatically scale EC2 instances based on demand?

• a. Amazon S3
• b. Amazon RDS
• c. Amazon CloudFormation
• d. Amazon EC2 Auto Scaling

Answer: d. Amazon EC2 Auto Scaling

Explanation: Amazon EC2 Auto Scaling is a service that automatically adjusts the number of EC2 instances in a group according to demand. It can help ensure that your application is always available to handle incoming traffic, while minimizing costs during periods of low demand. Amazon S3 is an object storage service, Amazon RDS is a relational database service, and Amazon CloudFormation is a service that allows you to model and provision AWS resources.

Which AWS service can be used to distribute incoming traffic to multiple endpoints based on specified rules?

• a. Amazon S3
• b. Amazon Route 53
• c. Amazon CloudFront
• d. Amazon Elastic Load Balancer

Answer: b. Amazon Route 53

Explanation: Amazon Route 53 is a highly available and scalable cloud DNS service that can be used to route traffic to various endpoints based on specified rules. It supports multiple routing policies, including weighted, latency-based, and failover routing. Amazon S3 is an object storage service, Amazon CloudFront is a content delivery network, and Amazon Elastic Load Balancer is a service that automatically distributes incoming application traffic across multiple targets.

Which AWS service can be used to deploy and manage Docker containers on a cluster of EC2 instances?

• a. Amazon EKS
• b. Amazon ECS
• c. Amazon ECR
• d. Amazon ElastiCache

Answer: b. Amazon ECS

Explanation: Amazon ECS (Elastic Container Service) is a fully managed service that makes it easy to run, stop, and manage Docker containers on a cluster of EC2 instances. It integrates with other AWS services like Elastic Load Balancing, EC2 Auto Scaling, and IAM for security and access control. Amazon EKS (Elastic Kubernetes Service) is another service for deploying and managing containerized applications using Kubernetes. Amazon ECR (Elastic Container Registry) is a fully-managed Docker container registry, and Amazon ElastiCache is a managed in-memory data store.

Which AWS service can be used to analyze and process large volumes of data in real time?

• a. Amazon S3
• b. Amazon Kinesis
• c. Amazon Redshift
• d. Amazon RDS

Answer: b. Amazon Kinesis

Explanation: Amazon Kinesis is a fully managed service that makes it easy to collect, process, and analyze real-time, streaming data at scale. It can be used to build custom applications that can respond to data in real time, and can be integrated with other AWS services like Lambda, S3, and DynamoDB. Amazon S3 is an object storage service, Amazon Redshift is a data warehousing service, and Amazon RDS is a relational database service.

Which AWS service can be used to store and manage secrets, such as database passwords and API keys?

• a. AWS Secrets Manager
• b. AWS IAM
• c. AWS Certificate Manager
• d. AWS KMS

Answer: a. AWS Secrets Manager

Explanation: AWS Secrets Manager is a service that enables you to easily store and manage secrets, such as database passwords and API keys. It can automatically rotate secrets to help meet compliance requirements, and can be integrated with other AWS services like RDS, DocumentDB, and Lambda. AWS IAM is a service that enables you to manage access to AWS resources, AWS Certificate Manager is a service that enables you to provision, manage, and deploy SSL/TLS certificates for use with AWS services, and AWS KMS is a managed encryption service.

Which AWS service can be used to run code in response to events, such as changes to objects in an S3 bucket?

• a. Amazon S3
• b. Amazon SQS
• c. AWS Lambda
• d. Amazon Kinesis

Answer: c. AWS Lambda

Explanation: AWS Lambda is a compute service that lets you run code in response to events, such as changes to objects in an S3 bucket or messages in an SQS queue. It automatically scales to handle any amount of traffic, and only charges you for the compute time that you consume. Amazon S3 is an object storage service, Amazon SQS is a managed message queue service, and Amazon Kinesis is a real-time data streaming service.

Which AWS service can be used to create and manage virtual private networks (VPNs) that connect your on-premises data centers to your AWS resources?

• a. Amazon VPC
• b. AWS Direct Connect
• c. Amazon Route 53
• d. AWS Firewall Manager

Answer: b. AWS Direct Connect

Explanation: AWS Direct Connect is a network service that enables you to create private, dedicated network connections between your on-premises data centers and your AWS resources. This can help reduce your network costs, increase bandwidth throughput, and provide a more consistent network experience than internet-based connections. Amazon VPC is a service that enables you to launch Amazon Web Services resources into a virtual network that you’ve defined, and AWS Firewall Manager is a service that centralizes the management of AWS WAF rules across multiple accounts and resources. Amazon Route 53 is a highly available and scalable cloud DNS service.

Q1)A business gathers information about the temperature, humidity, and air pressure in cities on many continents. The firm obtains 500 GB of data every day on average from each facility. Every location has a fast Internet connection. The business needs to swiftly compile the data from each of these international websites into a single Amazon S3 bucket. The answer needs to reduce operational complexity. Which option satisfies these criteria in AWS Certified Solutions Architect – Associate (SAA-C03) ?

• A. On the destination S3 bucket, enable S3 Transfer Acceleration. To transfer site data directly to the desired S3 bucket, use multipart uploads.
• B. Transfer the information from every site to an S3 bucket in the neighbourhood Region. For item replication to the target S3 bucket, use S3 Cross-Region Replication. After that, delete the data from the initial S3 bucket.
• C. Daily AWS Snowball Edge Storage Optimized device jobs should be scheduled to move data from each location to the nearest Region. For item replication to the target S3 bucket, use S3 Cross-Region Replication.
• D. Add each site’s data to an Amazon EC2 instance in the nearby Region. Put the information in a volume of the Amazon Elastic Block Store (Amazon EBS). Take an EBS snapshot and copy it to the Region containing the destination S3 bucket on a regular basis. the EBS volume in that Region be restored.

Correct Answer: A

Q2)The ability to examine the log files of a proprietary application is a requirement for a business. The logs are kept in an Amazon S3 bucket in JSON format. Simple, on-demand queries will be used. The analysis must be carried out by a solutions architect with the least amount of modifications to the current architecture. What should the solutions architect do to fulfil these criteria with the LEAST amount of administrative burden in AWS Certified Solutions Architect – Associate (SAA-C03) ?

• A. Load all the content into one location using Amazon Redshift, then execute the necessary SQL queries from there.
• B. Keep the logs in Amazon CloudWatch Logs. Run SQL queries from the Amazon CloudWatch console as necessary.
• C. Run the queries as necessary using Amazon Athena directly with Amazon S3.
• D. To organise the logs, use AWS Glue. Run the SQL queries on Amazon EMR using a temporary Apache Spark cluster.

Correct Answer: C

Q3)AWS Organizations are used by a firm to manage various AWS accounts for various departments. Project reports are stored in an Amazon S3 bucket under the management account. The business only wants users of accounts belonging to the organisation in AWS Organizations to have access to this S3 bucket. Which approach satisfies these criteria with the SMALLEST operational overhead in AWS Certified Solutions Architect – Associate (SAA-C03) ?

• A. Update the S3 bucket policy to include the aws PrincipalOrgID global condition key with a reference to the organisation ID.
• B. Establish a unit of organisation (OU) for each department. The S3 bucket policy should now include the aws:PrincipalOrgPaths global condition key.
• C. Track the InviteAccountToOrganization, LeaveOrganization, and RemoveAccountFromOrganization events using AWS CloudTrail. Adjust the S3 bucket policy as necessary.
• D. Assign a tag to each user who requires S3 bucket access. The S3 bucket policy should now include the aws:PrincipalTag global condition key.

Correct Answer: A

Q4)On an Amazon EC2 instance within a VPC, an application runs. The programme analyses log files that are kept in an Amazon S3 bucket. Without internet access, the EC2 instance must access the S3 bucket. Which option will link Amazon S3 to a private network inAWS Certified Solutions Architect – Associate (SAA-C03) ?

• A. Establish a gateway S3 endpoint in the VPC.
• B. Stream the logs to CloudWatch Logs on Amazon. Logs should be exported to an S3 bucket.
• C. On Amazon EC2, make an instance profile that permits S3 access.
• D. Establish a private link for the S3 endpoint in an Amazon API Gateway API.

Correct Answer: A

Q5)A business is employing a single Amazon EC2 instance to host a web application on AWS, and an Amazon EBS volume to store user-uploaded files. The business duplicated the architecture, generated a second EC2 instance and EBS volume in a different availability zone, and put both behind an application load balancer to improve scalability and availability. After this update was made, users complained that they could see one subset of their documents or the other every time they refreshed the website, but never all of their documents at once. How can users view all of their documents at once, according to a solutions architect’s recommendation in AWS Certified Solutions Architect – Associate (SAA-C03) ?

• A. Copies the data so that all documents are present in both EBS volumes.
• B. Set the Application Load Balancer to send users to the server hosting the documents.
• C. Transfer the information from the two EBS volumes to Amazon EFS. Change the programme so that fresh documents are saved to Amazon EFS
• D. Set the Application Load Balancer up so that it sends the request to both servers. Bring each document back from the proper server.

Correct Answer: C

Q6)Large video files are kept in on-site network attached storage by a corporation using NFS. The size of each video file varies from 1 MB to 500 GB. Storage capacity has reached 70 TB and is no longer increasing. The business makes the choice to move the video files to Amazon S3. In order to use the least amount of network traffic, the organisation must move the video files as quickly as possible. Which approach will satisfy these needs?

• A. Create an S3 bucket, first. Make a writeable IAM role with access to the S3 bucket. To locally copy all files to the S3 bucket, use the AWS CLI.
• B. Establish a Snowball Edge task in AWS. Get a Snowball Edge device on the property. To send data to the device, use the Snowball Edge client. Return the device so that Amazon S3 may receive the data from AWS.
• C. Install an S3 File Gateway locally. To connect to the S3 File Gateway, create a public service endpoint. Establish an S3 bucket. On the S3 File Gateway, create a brand-new NFS file share. Indicate the S3 bucket on the new file share. To the S3 File Gateway, move the data from the current NFS file share.
• D. Create a direct link between the on-premises network and AWS using AWS Direct Connect. Install an S3 File Gateway locally. To connect to the S3 File Gateway, create a public virtual interface (VIF). Establish an S3 bucket. On the S3 File Gateway, create a brand-new NFS file share. Indicate the S3 bucket on the new file share. To the S3 File Gateway, move the data from the current NFS file share.

Correct Answer: C

Q7)A business has a programme that processes incoming communications. Then, these messages are swiftly consumed by dozens of additional applications and microservices. The volume of texts varies greatly and occasionally spikes above 100,000 per second. The business wishes to boost scalability and decouple the solution. Which option satisfies these criteria?

• A. Keep sending the messages to Kinesis Data Analytics on Amazon. Set up the consumer applications so that they can read and handle the messages.
• B. To scale the number of EC2 instances based on CPU measurements, deploy the ingestion application on Amazon EC2 instances in an Auto Scaling group.
• C. Use a single shard to write the messages to Amazon Kinesis Data Streams. Messages can be preprocessed and saved in Amazon DynamoDB using an AWS Lambda function. Set up the consumer applications so they can process messages by reading from DynamoDB.

• D. Post the messages to a subject on an Amazon Simple Notification Service (Amazon SNS) that has multiple subscriptions to an Amazon Simple Queue Service (Amazon SOS). Set up the consumer programmes to handle messages from the queues.

Correct Answer: A

Q8)A distributed application is being migrated by a business to AWS. The workload served by the application is flexible. The central server of the old platform manages jobs across numerous compute nodes. The business wishes to update the application with a method that maximises scalability and resilience. How ought a solutions architect to create the architecture to satisfy these needs?

• A. Establish an Amazon Simple Queue Service (Amazon SQS) queue as the jobs’ final destination. Utilize Amazon EC2 instances that are control by an Auto Scaling group to implement the computing nodes. Scheduled scaling can be enable in EC2 Auto Scaling.
• B. Set up an Amazon Simple Queue Service (Amazon SQS) queue to serve as the jobs’ final destination. Utilize Amazon EC2 instances that are control by an Auto Scaling group to implement the computing nodes. Set up EC2 Auto Scaling according to the queue size.
• C. Use Amazon EC2 instances that are control by an Auto Scaling group to implement the main server and the compute nodes. Set up AWS CloudTrail as the jobs’ destination. Set up EC2 Auto Scaling to take into account the load on the main server.

• D. Use Amazon EC2 instances that are control by an Auto Scaling group to implement the main server and the compute nodes. Set the jobs’ destination to Amazon EventBridge (Amazon CloudWatch Events). Set up EC2 Auto Scaling according to how busy the compute nodes are.

Correct Answer: C

Q9)In a company’s data centre, an SMB file server is in operation. For the first few days after the files are created, the file server houses big files that are often request. The files are rarely access beyond 7 days. The overall amount of data is growing and is getting near to filling the company’s entire storage capacity. The company’s storage space has to be expand by a solutions architect without sacrificing low-latency access to the most recently accessed information. To prevent further storage problems, the solutions architect must additionally offer file lifecycle management. Which approach will satisfy these needs?

• A. Copy data from the SMB file server to AWS using AWS DataSync if it has been there for more than 7 days.
• B. Construct an Amazon S3 File Gateway to increase the business’s storage capacity. To move the data to S3 Glacier Deep Archive after 7 days, create an S3 Lifecycle policy.
• C. To increase the company’s storage capacity, develop an Amazon FSx for Windows File Server file system.
• D. Set up an application to access Amazon S3 on each user’s machine. To move the data to S3 Glacier Flexible Retrieval after 7 days, create an S3 Lifecycle policy.

Correct Answer: D

Q10)A business has an application that utilises an Amazon Aurora database and operates on Amazon EC2 instances. The user names and passwords for the EC2 instances are kept locally in a file, which they utilise to connect to the database. The business seeks to reduce the operational costs associated with credential management. What steps should a solutions architect take to reach this objective?

• A. Use AWS Secrets Manager. Set the rotation to automatic.
• B. AWS Systems Manager Parameter Store. Set the rotation to automatic.
• C. AWS Key Management Service (AWS KMS) encryption key-encrypt objects can be stored in an Amazon S3 bucket that has been create specifically for that purpose. The credential file should be move to the S3 bucket. Application should be point at the S3 bucket.
• D. For every EC2 instance, create an encrypted Amazon Elastic Block Store (Amazon EBS) volume. Each EC2 instance should have the new EBS volume attached. To the new EBS storage, migrate the credential file. Indicate the new EBS volume in the application.

Correct Answer: B

Q11)A multinational corporation uses Amazon EC2 instances to host its web application behind an Application Load Balancer (ALB). Both dynamic and static data are present in the web application. The business uses an Amazon S3 bucket to store its static data. Next, the business aims to boost efficiency and cut latency for both static and dynamic data. The business makes use of a custom domain name that is register through Amazon Route 53. What steps should a solutions architect take to fulfil these demands?

• A. Create a distribution on Amazon CloudFront with the S3 bucket and the ALB as origins. To direct traffic to the CloudFront distribution, configure Route 53.
• B. Establish an ALB as the origin of an Amazon CloudFront distribution. Make a normal accelerator for AWS Global Accelerator with the S3 bucket as an endpoint. To direct traffic to the CloudFront distribution, configure Route 53.
• C. Set up an S3 bucket as the origin of an Amazon CloudFront distribution. Make an ALB and a CloudFront distribution endpoint for an AWS Global Accelerator standard accelerator. An individual domain name should be made that points to the accelerator DNS name. Use the unique domain name as the web application’s endpoint.
• D. Establish an ALB as the origin of an Amazon CloudFront distribution. Make a normal accelerator for AWS Global Accelerator with the S3 bucket as an endpoint. two domain names should be made. For dynamic content, point one domain name to the CloudFront DNS name. For static content, point the other domain name to the accelerator DNS name. Use the domain names as the web application’s endpoints.

Correct Answer: C

Q14)A corporation maintains its AWS infrastructure on a monthly basis. The business must switch the login information for its Amazon RDS for MySQL databases among several AWS Regions throughout these maintenance procedures. Which approach will fulfil these demands with the LEAST operational burden in AWS Certified Solutions Architect – Associate (SAA-C03) ?

• A. Keep the credentials in AWS Secrets Manager as secrets. For the necessary Regions, utilise multi-Region secret replication. Rotate the secrets according to a schedule by configuring Secrets Manager.
• B. Create a secure string parameter in AWS Systems Manager and save the credentials there as secrets. For the necessary Regions, utilise multi-Region secret replication. Set up Systems Manager such that the secrets are change periodically.
• C. Keep the login information in a server-side encryption (SSE)-enabled Amazon S3 bucket. Invoke an AWS Lambda function using Amazon EventBridge (Amazon CloudWatch Events) to rotate the credentials.
• D. Use multi-Region customer controlled keys for AWS Key Management Service (AWS KMS) to encrypt the credentials as secrets. Put the secrets in a global table for Amazon DynamoDB. Take advantage of an AWS Lambda function to get the secrets out of DynamoDB. Rotate the secrets using the RDS API.

Correct Answer: A

Q15)Behind an Application Load Balancer, a business runs an e-commerce application on Amazon EC2 instances. The instances operate across various Availability Zones in an Amazon EC2 Auto Scaling group. Based on parameters related to CPU consumption, the Auto Scaling group scales. The transaction data is kept by the e-commerce application in a MySQL 8.0 database that is housed on a sizable EC2 instance. As the amount of applications increases, the database’s performance rapidly deteriorates. The application processes more read transactions than write ones. The business is looking for a solution that would automatically grow the database while ensuring high availability to handle the demand of fluctuating read workloads. Which approach will satisfy these needs in AWS Certified Solutions Architect – Associate (SAA-C03) ?

• A. Use a single node of Amazon Redshift for leader and computing functionality.
• B. Use a Single-AZ deployment using Amazon RDS. Set up Amazon RDS so that reader instances can be added in another Availability Zone.
• C. Implement a Multi-AZ deployment using Amazon Aurora. Aurora Auto Scaling can be configured using Aurora Replicas.
• D. Use EC2 Spot Instances and Amazon ElastiCache for Memcached.

Correct Answer: C

Q16)A business that has made the switch to AWS needs to put in place a solution to secure the traffic entering and leaving the production VPC. The business’ on-site data centre had an inspection server. The inspection server carried out particular tasks like traffic filtering and flow inspection. The business desires the identical features in the AWS Cloud. Which approach will satisfy these needs in AWS Certified Solutions Architect – Associate (SAA-C03) ?

• A. In the production VPC, use Amazon GuardDuty to inspect and filter traffic.
• B. Mirror traffic from the production VPC using traffic mirroring for traffic inspection and filtering.
• C. To establish the necessary rules for traffic inspection and traffic filtering for the production VPC, use AWS Network Firewall.
• D. Create the necessary traffic inspection and filtering rules for the production VPC using AWS Firewall Manager.

Correct Answer: C

Q17)On AWS, a business hosts a data lake. Data in Amazon S3 and Amazon RDS for PostgreSQL make up the data lake. The business requires a reporting solution with data visualisation and integration of all data sources in the data lake. All the visualisations should only be accessible to the company’s management staff. Only a small portion of the company’s employees should have access. Which approach will satisfy these needs in AWS Certified Solutions Architect – Associate (SAA-C03) ?

• A. Use Amazon QuickSight to create an analysis. Create new datasets by connecting all the data sources. Create dashboards to display data. Give the proper IAM roles access to the dashboards.
• B. Use Amazon QuickSight to create an analysis. Create new datasets by connecting all the data sources. Create dashboards to display data. Give the right individuals and groups access to the dashboards.
• C. Create a table and crawler for the data in Amazon S3 using AWS Glue. To generate reports, create an extract, transform, and load (ETL) job in AWS Glue. Reports should be published to Amazon S3. To restrict access to the reports, use the S3 bucket policies.
• D. Make a table and crawler for the data in Amazon S3 using AWS Glue. To gain access to data in Amazon RDS for PostgreSQL, use Amazon Athena Federated Query. Use Amazon Athena to produce reports. Reports should be publish to Amazon S3. To restrict access to the reports, use the S3 bucket policies.

Correct Answer: D

Q18)A business is putting a fresh business application into use. Two Amazon EC2 instances are used to execute the programme, and an Amazon S3 bucket is use to store documents. The S3 bucket must be accessible to the EC2 instances, according to a solutions architect. What steps should the solutions architect take to fulfil this demand in AWS Certified Solutions Architect – Associate (SAA-C03) ?

• A. Create an S3 bucket-accessible IAM role. Connect the EC2 instances to the role.
• B. Establish an S3 bucket access policy in an IAM policy. Connect the EC2 instances to the policy.
• C. Establish an S3 bucket access-granting IAM group. EC2 instances should be connected to the group.
• D. Make a user in IAM who has permission to access the S3 bucket. Connect the EC2 instances to the user account.

Correct Answer: A

Q19)A microservice that shrinks and compresses huge photos is being create by an application development team. The microservice should save an image that a user uploads via the web interface in an Amazon S3 bucket, process and compress the picture using an AWS Lambda function, and then store the compressed version of the image in another S3 bucket. A solutions architect must create a system that automatically processes the photos using dependable, stateless components. Which set of actions will be sufficient to fulfil these demands in AWS Certified Solutions Architect – Associate (SAA-C03) ? (Select two.)

• A. Establish a queue with Amazon Simple Queue Service (Amazon SQS). Set up the S3 bucket such that whenever an image is upload, a notification is sent to the SQS queue.
• B. Set the Amazon Simple Queue Service (Amazon SQS) queue as the invocation source for the Lambda function. Delete the message from the queue once the SQS message has been successfully process.
• C. Set up the Lambda function to check the S3 bucket periodically for fresh uploads. Write the file name of each detected uploaded image to a text file in memory, then utilise the text file to keep track of the processed images.

• D. Start an Amazon Simple Queue Service (Amazon SQS) queue monitoring Amazon EC2 instance. Log the file name in a text file on the EC2 instance and call the Lambda function when items are added to the queue.
• E. Set up a CloudWatch Events (Amazon EventBridge) event on Amazon to track the S3 bucket. Send a notification containing the application owner’s email address when an image is upload to an Amazon Simple Notification Service (Amazon SNS) topic for follow-up action.

Correct Answer: A and B

Q20)A three-tier web application for a company is set up on AWS. The web servers are set up in a VPC’s public subnet. The database servers and application servers are set up in separate private subnets within the same VPC. In an inspection VPC, the organisation has installed a virtual firewall appliance from the AWS Marketplace. An IP interface on the appliance is set up to accept IP packets. To analyse all application traffic before it reaches the web server, a solutions architect must integrate the appliance with the web application. Which approach will fulfil these demands with the LEAST operational burden in AWS Certified Solutions Architect – Associate (SAA-C03) ?

• A. Construct a Network Load Balancer in the application’s VPC’s public subnet to direct traffic to the packet inspection appliance.
• B. Setup a load balancer for the application in the application’s VPC’s public subnet to direct traffic to the appliance for packet inspection.
• C. Set up a transit gateway in the inspection VPConfigure route tables to be use as a conduit for incoming packets.
• D. Install a gateway load balancer in the VPC used for inspection. To receive the incoming packets and send them on to the appliance, create a Gateway Load Balancer endpoint.

Correct Answer: B

The post AWS Certified Solutions Architect – Associate (SAA-C03) Sample Questions appeared first on Testprep Training Tutorials.

As an AWS (PAS-C01) Certified: SAP on AWS – Specialist, you demonstrate your understanding of both SAP and AWS, which is necessary for architects, migrators, and administrators to manage SAP workloads on AWS. You can provide reliable, cost-effective solutions to your customers. Professionals who want to validate their SAP on AWS expertise as well as gain a comprehensive understanding of a customer’s landscape can achieve this certification.

Who can take this exam?

The AWS (PAS-C01) Certified: SAP on AWS – Specialty certification is ideal for those working in roles that require both SAP and AWS knowledge. The following items are recommended before taking this test:

• SAP experience of at least five years
• Working Experience with SAP on AWS for at least one year
• AWS Well-Architected Framework best practices, SAP certification requirements, and SAP support requirements are all met by SAP solutions running on AWS Cloud.

Recommended AWS knowledge

• High availability/disaster recovery  
• Core AWS infrastructure services  
• AWS migration tools  
• AWS global infrastructure  
• Security best practices 
• Multi-account scenarios and multi-Region scenarios  
• Operations and management services and tools  
• AWS transfer services 

Recommended SAP knowledge and other IT knowledge

• SAP Basis and SAP NetWeaver administration  
• SAP-supported databases (including SAP HANA)  
• SAP-supported operating systems (Linux and Windows)  
• SAP migration and installation tools  
• Sizing  Identity management 

Exam Format and Details

For more details check: AWS (PAS-C01) Certified: SAP on AWS – Specialty FAQ

Course Outline: AWS (PAS-C01)

The AWS (PAS-C01) Certified: SAP on AWS – Specialty Exam covers the following topics – 

Domain 1: Design of SAP workloads on AWS

1.1 Design the AWS account structure and connectivity patterns for SAP workloads on AWS.

Knowledge of:

• AWS global infrastructure (AWS Documentation: Global infrastructure)
• Account strategy for SAP workloads (AWS Documentation: Assess the need for specific security controls)
• VPC patterns for SAP workloads (AWS Documentation: SAP on AWS Planning)
• SAP connectivity strategies (for example, AWS Direct Connect, AWS VPN, SAProuter, SAP GUI, Amazon AppStream) (AWS Documentation: SAP Support Access, connect SAP solutions running on AWS with AWS accounts and services)

Skills in:

• Evaluating the use of a single AWS account versus the use of multiple AWS accounts (AWS Documentation: Benefits of using multiple AWS accounts)
• Evaluating the use of a single VPC versus the use of multiple VPCs, including user restrictions with VPC sharing (AWS Documentation: Share your VPC with other accounts, VPC sharing)
• Evaluating on-premises, co-location, and cloud integration connectivity options (AWS Documentation: Network Transformation on AWS, AWS Direct Connect Integration with VMware Cloud on AWS)
• Defining AWS Regions and Availability Zones based on network and latency requirements (AWS Documentation: Choose Regions and Availability Zones to minimize latency, Regions and Zones)
• Designing connectivity options between different AWS accounts and different VPCs (AWS Documentation: Amazon VPC-to-Amazon VPC connectivity options)

1.2 Design a secure solution for hosting SAP workloads on AWS.

Knowledge of:

• AWS Identity and Access Management (IAM) (AWS Documentation: What is IAM?)
• Route tables, security groups, and network ACLs (AWS Documentation: Control traffic to subnets using Network ACLs, Configure route tables, Control traffic to resources using security groups)
• Encryption options for data at rest and data in transit (AWS Documentation: Encrypting Data-at-Rest and -in-Transit)
• AWS service endpoints (AWS Documentation: AWS service endpoints)

Skills in:

• Defining IAM users and roles for SAP workloads on AWS (AWS Documentation: Manage privileged access to your SAP workload)
• Defining inbound and outbound network flows by using security group rules and network ACL rules (AWS Documentation: Control traffic to subnets using Network ACLs, Control traffic to resources using security groups)
• Troubleshooting traffic flow by using AWS native tools (AWS Documentation: VPC Traffic Mirroring, Using VPC Traffic Mirroring to monitor and secure your AWS)
• Defining the encryption strategy for data at rest and data in transit (AWS Documentation: Encrypting Data-at-Rest and -in-Transit)
• Defining service endpoints for service integrations (AWS Documentation: AWS service endpoints)

1.3 Define optimized and cost-effective infrastructure solutions for SAP workloads on AWS.

Knowledge of:

• Certified operating system releases for SAP (AWS Documentation: Supported versions for SAP deployments, SAP on AWS Planning)
• Certified database release versions for SAP (AWS Documentation: Supported versions for SAP deployments)
• Certified block storage solutions for SAP (AWS Documentation: Storage Configuration for SAP HANA)
• Certified instance types (AWS Documentation: Amazon EC2 Instance Types)
• Best practices to define shared storage solutions (AWS Documentation: Shared storage (Amazon S3 and Amazon EFS))
• AWS pricing models (AWS Documentation: Understand the fundamentals of pricing, AWS Pricing)
• SAP transport strategy

Skills in:

• Defining SAP certified solutions on AWS based on the operating system, database, and SAP combinations (AWS Documentation: SAP on AWS Planning)
• Selecting the optimal instance family for SAP workloads (AWS Documentation: Select EC2 instances suitable for SAP workloads)
• Defining instance sizing based on SAP Application Performance Standard (SAPS) performance measurements, database sizing tools, and SAP Early Watch Alert (EWA) reports (AWS Documentation: Evaluate or estimate performance requirements, Sizing)
• Defining the right storage selection for SAP workloads (AWS Documentation: Select the optimal storage solution)
• Defining shared storage solutions for SAP file systems (AWS Documentation: Create mount points and volume associations to align with function)
• Evaluating AWS pricing models (AWS Documentation: Understand the fundamentals of pricing, AWS Pricing)
• Evaluating a shared solution versus a dedicated solution for database licensing (AWS Documentation: Evaluate licensing impact and optimization options)
• Evaluating dedicated hardware solution benefits (for example, SAP certified Nitro based EC2 instance) (AWS Documentation: Amazon EC2 Instance Types, Select EC2 instances suitable for SAP workloads)

1.4 Design highly resilient solutions for SAP workloads on AWS.

Knowledge of:

• High-availability solution options for SAP workloads on AWS (AWS Documentation: SAP on AWS High Availability Setup)
• Disaster recovery solution options for SAP workloads on AWS (AWS Documentation: High availability and disaster recovery)

Skills in:

• Defining the optimal architecture by considering operating system, database, and application cluster requirements (AWS Documentation: Define an approach to maintain availability, Follow operating system guidelines for SAP performance)
• Designing single and distributed SAP solutions on AWS infrastructure (AWS Documentation: Architecture guidelines and decisions )
• Designing highly available solutions for SAP systems based on availability requirements (AWS Documentation: High availability and disaster recovery, SAP on AWS Planning)
• Designing disaster recovery solutions for SAP systems based on recovery time objective (RTO) and recovery point objective (RPO) requirements (AWS Documentation: Plan for Disaster Recovery (DR))
• Defining the optimal architecture by considering scale-up and scale-out options (AWS Documentation: Review the size, granularity, and latest available EC2 instances)
• Evaluating instance placement options for availability improvement (AWS Documentation: Choose Regions and Availability Zones to minimize latency, Select EC2 instances suitable for SAP workloads)

Domain 2: Implementation of SAP workloads on AWS

2.1 Deploy databases for SAP workloads on AWS.

Knowledge of:

• Administration of operating systems (for example, Linux, Windows) (AWS Documentation: Supported operating systems)
• File system layout of databases (AWS Documentation: Database)
• AWS network concepts (AWS Documentation: Amazon VPC)
• Database administration and security

Skills in:

• Installing database systems (AWS Documentation: Getting started with Amazon RDS)
• Installing database clients (AWS Documentation: Connecting to an Amazon RDS DB instance)

2.2 Deploy SAP applications on AWS.

Knowledge of:

• Administration of operating systems (for example, Linux, Windows) (AWS Documentation: Supported operating systems)
• File system layout of SAP applications (AWS Documentation: Disk Layout)
• AWS network concepts (AWS Documentation: Amazon VPC)
• SAP Basis and SAP security (AWS Documentation: SAP on AWS Overview)

Skills in:

• Installing SAP applications (AWS Documentation: Install SAP systems)
• Configuring SAP applications

2.3 Configure high availability for SAP workloads.

Knowledge of:

• AWS global infrastructure (AWS Documentation: Global infrastructure)
• Administration of operating systems and databases (AWS Documentation: Working with Amazon RDS Custom, Database)
• SAP certified high-availability solutions on AWS (AWS Documentation: SAP on AWS High Availability Setup)
• AWS networking concepts (for example, Amazon Route 53, overlay IP addresses, routing methods) (AWS Documentation: Amazon Route 53 concepts, working with a private hosted zone)
• High-availability cluster concepts (AWS Documentation: High availability and scalability on AWS)

Skills in:

• Evaluating SAP-certified high-availability solutions (AWS Documentation: Define an approach to maintain availability)
• Configuring a highly available cluster between ABAP SAP Central Services (ASCS) and Enqueue Replication Server (ERS) nodes (AWS Documentation: High Availability System Deployment)
• Configuring a highly available cluster between database nodes (AWS Documentation: Create a high-availability database cluster)
• Performing cluster failover tests (AWS Documentation: FailoverDBCluster)

2.4 Configure the disaster recovery setup for SAP workloads.

Knowledge of:

• AWS global infrastructure (AWS Documentation: Global infrastructure)
• Administration of operating systems and databases (AWS Documentation: Working with Amazon RDS Custom, Database)
• AWS networking concepts (for example, Route 53, routing methods) (AWS Documentation: Amazon Route 53 concepts)
• RTO and RPO (AWS Documentation: Plan for Disaster Recovery (DR))
• Disaster recovery scenarios (for example, backup and restore, pilot light, warm standby, multi-site) (AWS Documentation: Disaster recovery options in the cloud)
• Disaster recovery solutions on AWS

Skills in:

• Configuring disaster recovery solutions (AWS Documentation: Disaster recovery options in the cloud)
• Configuring database replication (AWS Documentation: Working with read replicas)
• Performing disaster recovery testing

2.5 Automate deployments of SAP workloads.

Knowledge of:

• Infrastructure as code (IaC) (for example, AWS CloudFormation) (AWS Documentation: Infrastructure as Code)
• Configuration management tools (for example, AWS Systems Manager) (AWS Documentation: AWS Systems Manager Documents)
• AWS Launch Wizard for SAP (AWS Documentation: AWS Launch Wizard for SAP)
• DevOps tools (AWS Documentation: Introduction to DevOps on AWS)

Skills in:

• Automating infrastructure deployments by using IaC (AWS Documentation: Infrastructure as Code)
• Automating SAP application installations (AWS Documentation: Automate SAP application software installation with AWS Launch Wizard)
• Automating SAP deployments by using Launch Wizard (AWS Documentation: Deploy an SAP application with AWS Launch Wizard)
• Using configuration management tools (AWS Documentation: Inventory and Configuration Management)

2.6 Validate AWS infrastructure for hosting SAP workloads.

Knowledge of:

• Administration of operating systems (for example, Linux, Windows) (AWS Documentation: Supported operating systems)
• Database file system layout (AWS Documentation: Create mount points and volume associations to align with function)
• AWS network concepts (AWS Documentation: Amazon VPC)
• Database administration and security (AWS Documentation: Security in Amazon RDS)
• Performance baseline for SAP (AWS Documentation: Establish baseline performance requirements)

Skills in:

• Performing tests with the HANA Hardware and Cloud Measurement Tools (HCMT)
• Reviewing instance families and sizes (AWS Documentation: Amazon EC2 Instance Types)
• Validating dedicated hardware solution benefits (for example, SAP certified Nitro based EC2 instance) (AWS Documentation: Migrating SAP HANA on AWS to an EC2 High Memory Instance, Amazon EC2 instance types for SAP on AWS)
• Performing license checks for infrastructure for SAP workloads on AWS (AWS Documentation: Evaluate licensing impact and optimization options)
• Performing storage checks by using the flexible I/O tester (FIO) and the dd command (AWS Documentation: Benchmark EBS volumes, I/O characteristics and monitoring, Initialize Amazon EBS volumes)
• Performing network latency tests (AWS Documentation: Network Architecture Selection)
• Validating infrastructure (for example, Well-Architected Review, SAP OSS Notes, certified operating systems, relational database management system combinations) (AWS Documentation: Build and protect the operating system, SAP Lens for the AWS Well-Architected Framework)

Domain 3: Migration of SAP workloads to AWS

3.1 Determine the optimal migration approach for SAP workloads to AWS.

Knowledge of:

• Homogeneous migration process and heterogeneous migration process (AWS Documentation: Heterogeneous database migration, Tools for homogeneous database migrations)
• Target SAP environment architecture (operating system, database, and application) (AWS Documentation: SAP on AWS Planning)
• SAP interfaces and integration
• Data migration tools (AWS Documentation: AWS Database Migration Service)
• Data transfer options
• DNS and AWS networking services (for example, Direct Connect, Site-to-Site VPN, Route 53) (AWS Documentation: Resolving DNS queries between VPCs and your network, Amazon Route 53 FAQs)
• AWS storage services (for example, Amazon Elastic File System [Amazon EFS], Amazon FSx, Amazon S3) (AWS Documentation: Storage)
• AWS compute services (for example, Amazon EC2) (AWS Documentation: Amazon EC2)
• AWS directory services specific to SAP workloads on Windows (AWS Documentation: AWS Directory Service)

Skills in:

• Creating a technical migration and cutover plan (AWS Documentation: Cut over)
• Determining the suitable tools and methodologies for cloud migration (AWS Documentation: Choosing a migration method)
• Evaluating the compatibility for target SAP environments on AWS

3.2 Perform a homogeneous migration of SAP workloads to AWS.

Knowledge of:

• Migration process methodologies for homogeneous migration (for example, backup and restore, database replication, block-level replication) (AWS Documentation: Migration Tools and Methodologies)
• AWS data transfer services (for example, AWS Snowball, AWS DataSync, S3 Transfer Acceleration) (AWS Documentation: AWS DataSync FAQs, Best practices for accelerating data migrations using AWS Snowball Edge, Amazon S3 Transfer Acceleration)
• DNS and AWS networking services (for example, Direct Connect, Site-to-Site VPN, Route 53) (AWS Documentation: Resolving DNS queries between VPCs and your network, Set up integrated DNS resolution for hybrid networks)
• AWS storage services (for example, Amazon EFS, Amazon FSx, Amazon S3) (AWS Documentation: Storage)
• AWS compute services (for example, Amazon EC2) (AWS Documentation: Amazon EC2)

Skills in:

• Using SAP and database-specific migration tools (for example, backup and restore, database replication) (AWS Documentation: Migration Tools and Methodologies)
• Using AWS native tools for migrations (for example, AWS Server Migration Service [AWS SMS], AWS Application Migration Service [CloudEndure Migration]) (AWS Documentation: Choosing a Rehost Migration Tool – CloudEndure or AWS SMS, AWS Server Migration Service)

3.3 Perform a heterogeneous migration of SAP workloads to AWS.

Knowledge of:

• Migration process methodologies for heterogeneous migration (for example, SAP export/import with Software Provisioning Manager (SWPM), Software Update Manager – Database Migration Option (SUM-DMO) with System Move, third-party vendor tools) (AWS Documentation: Using the SAP Database Migration Option (DMO) to Migrate to AWS)
• AWS data transfer services (for example, Snowball, DataSync, S3 Transfer Acceleration) (AWS Documentation: AWS DataSync FAQs, Best practices for accelerating data migrations using AWS Snowball Edge, Amazon S3 Transfer Acceleration)
• Migration from any DB to SAP HANA (AWS Documentation: Migrating SAP HANA from Other Platforms to AWS)
• DNS and AWS networking services (for example, Direct Connect, Site-to-Site VPN, Route 53, DNS) (AWS Documentation: Resolving DNS queries between VPCs and your network, Set up integrated DNS resolution for hybrid networks)
• AWS storage services (for example, Amazon EFS, Amazon FSx, Amazon S3) (AWS Documentation: Storage)
• AWS compute services (for example, Amazon EC2) (AWS Documentation: Amazon EC2)

Skills in:

• Using SAP and database-specific migration tools (for example, export/import, SWPM, SUM-DMO) (AWS Documentation: Using the SAP Database Migration Option (DMO) to Migrate to AWS)
• Using AWS native tools for migrations (for example, AWS SMS, AWS Application Migration Service [CloudEndure Migration]) (AWS Documentation: AWS Application Migration Service, Choosing a Rehost Migration Tool – CloudEndure or AWS SMS)

3.4 Optimize the migration of SAP workloads.

Knowledge of:

• Migration process methodologies (AWS Documentation: migration process)
• Data transfer optimization (AWS Documentation: Plan for Data Transfer)
• Network optimization (AWS Documentation: Networking in Amazon EC2)
• AWS networking services (for example, Direct Connect, Site-to-Site VPN, Route 53) (AWS Documentation: Connect your VPC to remote networks using AWS Virtual Private Network, Resolving DNS queries between VPCs and your network, Networking and Content Delivery)
• AWS storage services (for example, Amazon EFS, Amazon FSx, Amazon S3) (AWS Documentation: Storage)
• AWS compute services (for example, Amazon EC2) (AWS Documentation: Amazon EC2)
• AWS automation tools (for example, Launch Wizard, CloudFormation, Systems Manager) (AWS Documentation: Manage Launch Wizard application resources, AWS Systems Manager Automation)

Skills in:

• Deploying target SAP environments in an automated way (AWS Documentation: Automating system provisioning)
• Fine-tuning data transfer (AWS Documentation: Change processing tuning settings)
• Architecting for migration acceleration (AWS Documentation: Amazon S3 Transfer Acceleration)

Domain 4: Operation and maintenance of SAP workloads on AWS

4.1 Monitor the underlying infrastructure of SAP environments on AWS for performance, availability, and security.

Knowledge of:

• Performance monitoring of AWS services for SAP (AWS Documentation: Implement infrastructure monitoring for SAP)
• Availability monitoring of AWS services for SAP (AWS Documentation: Monitoring)

Skills in:

• Configuring custom metrics and alarms for SAP (for example, Amazon CloudWatch) (AWS Documentation: Create alarms for custom metrics using Amazon CloudWatch, Creating custom CloudWatch metrics and alarms)
• Configuring alarm notifications and invoking actions (for example, Amazon Simple Notification Service [Amazon SNS]) (AWS Documentation: Setting up Amazon SNS notifications, Using AWS Lambda with Amazon Simple Notification Service)
• Installing and updating AWS Data Provider for SAP (AWS Documentation: AWS Data Provider for SAP)
• Monitoring API calls for accounts (for example, AWS CloudTrail) (AWS Documentation: Logging AWS Account Management API calls using AWS CloudTrail)
• Monitoring and invoking responses for alerts (for example, Amazon GuardDuty) (AWS Documentation: Creating custom responses to GuardDuty findings with Amazon CloudWatch Events, Understanding Amazon GuardDuty findings)

4.2 Manage the data protection of SAP applications by using AWS native services.

Knowledge of:

• RTO and RPO (AWS Documentation: Plan for Disaster Recovery (DR))
• Backup and recovery strategies for SAP databases and applications (AWS Documentation: Backup and Recovery)
• Protection of data at rest and data in transit (for example, data encryption) (AWS Documentation: Encrypting Data-at-Rest and -in-Transit)
• Network traffic logging, monitoring, threat detection, and analytics (AWS Documentation: Continuously monitor network traffic and resources, Event logging monitoring, auditing and logging, Rich Monitoring and Logging)
• IAM IAM)

Skills in:

• Configuring and managing backup and restore of SAP databases by using database native tools and AWS Backing Agent for databases (AWS Documentation: Install and configure AWS Backint Agent for SAP HANA, AWS Backint Agent)
• Managing S3 Lifecycle policies (AWS Documentation: Managing your storage lifecycle)
• Configuring and managing Amazon Elastic Block Store (Amazon EBS) snapshots and Amazon Machine Images (AMIs) (AWS Documentation: Amazon Machine Images (AMI), Instances and AMIs)
• Automating backup of SAP components on AWS (for example, AWS Backup, AWS Storage Gateway) (AWS Documentation: AWS Backup, Amazon S3 File Gateway)
• Configuring encryption for AWS storage and backup services and tools (AWS Documentation: Encryption for backups in AWS Backup, Protecting data using encryption)
• Creating and managing accounts, users, groups, access policies, and roles in IAM (AWS Documentation: Policies and permissions in IAM, IAM Identities (users, user groups, and roles))
• Implementing detective controls (for example, CloudTrail, CloudWatch, GuardDuty) (AWS Documentation: Detection, Understanding Amazon GuardDuty findings)

4.3 Perform routine and proactive maintenance activities for SAP applications on AWS.

Knowledge of:

• Patch management of different operating systems, SAP applications, and databases (AWS Documentation: Regularly perform patch management for software currency, Build and protect the operating system, Follow operating system guidelines for SAP performance)
• Downtime management of SAP systems on AWS (AWS Documentation: Operating System Maintenance)
• Basics of Linux and Windows clustering (AWS Documentation: Get started with Amazon EC2 Windows instances)
• Architectures and administration for high availability and disaster recovery (AWS Documentation: High availability and disaster recovery, Disaster recovery options in the cloud)

Skills in:

• Configuring and automating patching by using Systems Manager Patch Manager (AWS Documentation: AWS Systems Manager Patch Manager)
• Managing downtime schedules by using Systems Manager maintenance windows (AWS Documentation: AWS Systems Manager Maintenance Windows, Maintenance window scheduling and active period options, patching schedules using maintenance windows)
• Defining maintenance actions by using Systems Manager documents (AWS Documentation: AWS Systems Manager Documents, AWS Systems Manager Maintenance Windows
• Restoring data from AWS sources (for example, EBS snapshots, AMIs) (AWS Documentation: Amazon EC2 backup and recovery with snapshots and AMIs)
• Operating and maintaining high-availability architectures (for example, application failovers, database failovers) (AWS Documentation: Multi-AZ deployments for high availability, Using failover in an Amazon Aurora global database)
• Maintaining a disaster recovery landscape and performing disaster recovery drills (AWS Documentation: Disaster recovery options in the cloud, Disaster recovery overview)
• Performing operations as code (for example, CloudFormation, Systems Manager, AWS Lambda) (AWS Documentation: AWS Code Sample Catalog, AWS::SSM::Document, AWS::Lambda::Function)

4.4 Review and optimize the architecture of SAP environments on AWS on a regular basis.

Knowledge of:

• AWS cost and usage monitoring tools (for example, AWS Trusted Advisor, AWS Pricing Calculator) (AWS Documentation: AWS Cost Explorer, AWS Trusted Advisor)
• Storage configurations and utilization monitoring for SAP workloads on AWS (AWS Documentation: Implement infrastructure monitoring for SAP, Implement workload configuration monitoring)
• EC2 instance sizing for SAP applications and databases (AWS Documentation: Amazon EC2 instance types for SAP on AWS, Select EC2 instances suitable for SAP workloads)

Skills in:

• Performing SAP capacity planning and reconfiguring (AWS Documentation: AWS services SAP on AWS Planning)
• Monitoring cost and usage with AWS tools (for example, Cost Explorer, AWS Budgets) (AWS Documentation:  AWS Cost Explorer, Analyzing your costs with AWS Cost Explorer, AWS Budgets)

Preparation Guide for AWS (PAS-C01) Certified: SAP on AWS – Specialty Exam

Upon reviewing this Preparation Guide, you will gain a better understanding of AWS (PAS-C01) Certified: SAP on AWS – Specialty exam objectives, understand the exam pattern, and strategize your preparation accordingly. A list of resources along with the exam requirements is provided along with an explanation of the exam domains. This preparatory guide will help you pass the AWS (PAS-C01) exam without any hassle.

Learning Resource 1 – AWS Certified: SAP on AWS – Specialty Exam Guide

Studying the Official Study Guide thoroughly is important if you want to pass the AWS (PAS-C01) exam. In order to avoid missing out on any important information, you must understand all topics covered in the syllabus.

• Design of SAP workloads on AWS
• Implementation of SAP workloads on AWS
• Migration of SAP workloads to AWS
• Operation and maintenance of SAP workloads on AWS

Training built by AWS experts – SAP on AWS (Technical)

AWS is an ideal platform for running SAP. During this 4hour fundamental course, you will learn about the technical fundamentals and key architectural patterns. The course covers how to size SAP on AWS to meet performance requirements, performing basic system operations on SAP on AWS, including backups and monitoring, as well as making architectural decisions in accordance with AWS recommendations.

AWS Whitepapers

You can enhance your technical knowledge by reading one of the whitepapers offered by the AWS team. We produce these whitepapers exclusively with the help of AWS analysts and other AWS partners. We have compiled a list of AWS whitepapers that you might find useful:

• SAP Lens – AWS Well-Architected Framework
• High Availability and Disaster Recovery Options
• SAP on AWS Technical Documentation

Join Online Forums

The benefits of a healthy discussion are universal, regardless of where it occurs. Having the opportunity to discuss issues and learn how their competitors are doing in the exams is a great way for students to get insights into their competition. A benefit of anything that is available online is the number of people who can take part in it. Offline discussions are limited to a small group, while online discussions can reach a larger audience.

Solve Practice Tests

Practice papers prove beneficial in all aspects. You’ll feel more confident about the exam if you get more questions right in practice tests. It will also be easier for you to gauge how much time and effort is needed in different sections of the exam. You will train your brain to function at its most efficient level throughout the exam. So start preparing for the AWS (PAS-C01) Certified: SAP on AWS – Specialty Now!

Sample Questions: AWS Certified: SAP on AWS – Specialty (PAS-C01)

The post AWS (PAS-C01) Certified: SAP on AWS-Specialty appeared first on Testprep Training Tutorials.

Taking the AWS (PAS-C01) Certified: SAP on AWS – Specialty exam will help you gain a fundamental understanding of both SAP and AWS for managing SAP workloads on AWS. The ones planning to take this exam must check out the AWS (PAS-C01) FAQ, to gain a better understanding of the exam and its objectives.

Top AWS (PAS-C01) FAQs

Are there any mandatory training or exam requirements to take the AWS (PAS-C01) Exam?

No there is no compulsion to take any training for the exam. However, AWS recommends the SAP on AWS (Technical) training program for a better understanding of the course.

What is the retake policy?

If you are unable to pass the exam, then you must wait 14 days before becoming eligible to retake the exam. Until you pass the test, there is no limit to the number of exam attempts. But, for each re-attempt, you must pay the full registration price fee. Also, the Beta exam test-takers will get one attempt only.

When will I get my result?

Right after completing your exam, a pass or fail notification will be visible on the testing screen. Also, candidates will receive an email confirming their exam completion. AWS will present a detailed exam result within five business days of completing your exam. This exam detail will appear on your AWS Certification Account of yours, under Previous Exams.

What are the service and features covered in the exam?

AWS does not publish the services and features in its certification exam. The exam guide covers the current topic areas and objectives for the exam. See AWS Exam guides, to know more.

Are there benefits to AWS Certification holders?

AWS offers several benefits to its certified members, apart from validating their skills. See the AWS Certification Benefits page to get a complete list of AWS benefits.

Which certification programs are available to take from home or office with online proctoring?

AWS offers its certification exams via online proctoring as well. AWS uses Pearson VUE, a third-party test delivery provider for its online proctoring exams. Visit the Pearson VUE site, to learn more about AWS online proctored certification exams.

About AWS Certification

How do I become AWS certified?

In order to become AWS certified, you must get a passing score in the AWS proctored exam, and attain your AWS Certification. After getting a passing score, AWS will send you your certification credentials. 

How long will AWS certification be valid?

AWS-certified individuals should get their certification recertified, every three years. See the AWS Certification Recertification page for more details.

What is the difference between AWS Certification and AWS Exam?

AWS exam refers to a test that validates your technical knowledge of AWS products and services. On the other hand, AWS certification is a credential that you earn upon successfully passing an AWS exam. You will earn a digital badge and title that you can use on business cards and other professional collateral to designate yourself as AWS Professional.

Exam Resources for AWS (PAS-C01) FAQs

What is the best way to prepare for the AWS (PAS-C01) Exam?

An ideal way to prepare for AWS (PAS-C01) certification exams is to get practical experience. AWS recommends having a minimum of six months to two years of experience with the AWS platform. AWS also provides several training programs and resource materials to help in preparation.

Check the AWS Certification Preparation page for AWS recommended training, practice exams, exam guides, whitepapers, sample questions, and other resources.

Does AWS offer practice tests for AWS Certification?

Yes, AWS offers practice exams for all its certification Exams. These will test your knowledge of the AWS platform. You can purchase these Practice exams from AWS exam delivery providers via your AWS Certification Account. The Foundational and Associate-level practice exams will cost you 20 USD, while the Professional and Specialty practice exams will cost you 40 USD.

For how long is the practice exam available for AWS Certification?

Your access to the AWS practice exam expires in 180 days. After launching the exam, you will get only 30 days to complete the exam. Else, the allotted practice exam time expires. However, you can pause your practice exam by shutting down your exam browser. Selecting “End Test” will mark the exam complete and it cannot be restarted. 

How will get my score for the practice exam?

This will be similar to the actual exam result.

How to find the test centers near me?

You can find test centers with the following options –

• For PSI test centers visit https://awsavailability.psiexams.com.
• For Pearson VUE test centers visit https://home.pearsonvue.com/test-taker.aspx

Exam Scoring and Content

How many questions should I answer to receive a passing score

The passing scores of AWS certifications are set using statistical analysis and can change. AWS does not provide the exam passing scores as they go through modifications to reflect the changes in the test pattern, with the updates in the exam content.

How often are exams updated?

AWS rotates its questions in and out, on a regular basis. This is done in adherence to the exam guide. The major revisions to an exam will be made public by AWS, via the Exam guide.

When AWS releases a new product or service, How soon will it appear on the exam?

Any new product, service, or feature AWS will generally get available, 6 months prior to it appearing on a certification exam.

Note – This applies only to AWS certification exams, and not AWS training.

If an existing feature or service has changed. How will that reflect in the exam?

The AWS certification team will replace the exam questions, which are impacted by any change. 

How should I answer a question that I think has been affected by a change in service or product?

You must choose the best available answer from the given options in the question. 

The post AWS (PAS-C01) Certified: SAP on AWS – Specialty FAQ appeared first on Testprep Training Tutorials.

The AWS Certified DevOps Engineer Professional certification exam’s objective is to assess a candidate’s technical competence in configuring, managing, and supervising distributed application systems on the AWS platform. The article provides a list of AWS Certified DevOps Engineer Sample Questions that cover core exam topics including –

• SDLC Automation
• Configuration Management and Infrastructure as Code
• Monitoring and Logging
• Policies and Standards Automation
• Incident and Event Response 
• High Availability, Fault Tolerance, and Disaster Recovery

Q1)What does a circular dependency mean in AWS CloudFormation?

• A. When a Template makes a reference to a previous iteration of itself.
• B. When Nested Stacks are interdependent.
• C. When a DependOn loop forms between Resources.
• D. When a Template mentions a region, and the original Template is referenced.

Correct Answer: C

Refer: What is AWS CloudFormation?

Q2)Regarding AWS CloudFormation, which of the following claims is accurate?

• A. The default timeout for custom resources that use SNS is three minutes.
• B. A code>ServiceToken/code> attribute is not necessary for custom resources that use SNS.
• C. Unique resources created using Lambda and code>
• Code.ZipFile enables inline resource compilation for Node.js.
• D. Lambda does not require a code>ServiceToken/code> field for custom resources.

Correct Answer: C

Q3)Which of the following claims is true in terms of compliance and security?

• A. Access keys for AWS IAM Users never need to be rotated.
• B. Neither AWS IAM Users nor AWS IAM Roles ever require you to rotate access keys.
• C. None of the other claims are accurate.
• D. Access keys for AWS IAM Roles never need to be rotated.

Correct Answer: D

Explanation: You do not need to rotate IAM Role Access Keys because AWS will do it on your behalf. Through the use of the security credentials linked to the role, the application is given access to the actions and resources that you have defined for the role. We automatically rotate these temporary security credentials. At least five minutes before the old credentials expire, we make fresh credentials available.

Refer: IAM roles for Amazon EC2

Q4)Which of the above parameters needs to be set in order to prevent file system damage when a user attaches an EBS volume to a new instance after detaching it from an existing one?

• A. Unmount the volume first
• B. Stop all the I/O of the volume before processing
• C. Take a snapshot of the volume before detaching
• D. Force Detach the volume to ensure that all the data stays intact

Correct Answer: A

Q5)Which language cannot be used to build customised Ansible modules?

• A. Python
• B. C++
• C. Bash
• D. All of the languages listed are supported

Correct Answer: D

Q6)Where in a playbook’s “vars” section should a variable that has been assigned be overridden?

• A. Inventory group var
• B. playbook host_vars
• C. role defaults
• D. extra vars

Correct Answer: D

Q7)When thinking about AWS Elastic Beanstalk, which of the following assertions is accurate?

• A. Worker tiers pull jobs from SNS.
• B. Worker tiers pull jobs from HTTP.
• C. Worker tiers pull jobs from JSON.
• D. Worker tiers pull jobs from SQS.

Correct Answer: D

Q8)How ought a playbook to be appropriately launched (in accordance with best practises)?

• A. – hosts: all
• B. …
• C. ###
• D. —

Correct Answer: D

Q9)Which of the following is the appropriate syntax to use when passing multiple variable names and values to a playbook’s command line?

• A. ansible-playbook playbook.yml -e `host=”foo” pkg=”bar”‘
• B. ansible-playbook playbook.yml -e `host: “foo”, pkg: “bar”‘
• C. ansible-playbook playbook.yml -e `host=”foo”‘ -e `pkg=”bar”‘
• D. ansible-playbook playbook.yml –extra-vars “host=foo”, “pkg=bar”

Correct Answer: A

Q10)Which deployment method, when using AWS Auto Scaling Groups and Auto Scaling Launch Configurations, provides the quickest time to live for individual servers?

• A. Pre-baking AMIs with complete code and deployment setup.
• B. Launching an instance while using a Dockerfile bootstrap.
• C. Using bootstrapping scripts for UserData.
• D. SSHing into fleets dynamically using AWS EC2 Run Commands.

Correct Answer: A

Q11) Which of the following statements about AWS OpsWorks is true?

• A. Both layers and stacks have numerous levels.
• B. Stacks and instances both have a large number of layers.
• C. Stacks have numerous instances, and layers have many stacks.
• D. Stacks have numerous instances, and instances have numerous layers.

Correct Answer: A

Q12)What holds true for DynamoDB’s Local Secondary Key attributes?

• A. Only one of the two keys—the sort key or the partition key—can be different from the table.
• B. The table can only differ from the sort key.
• C. The partition key and sort key may not match the table exactly.
• D. The only key that can differ from the table is the partition key.

Correct Answer: B

Q13)When deploying to a Docker swarm, which section of the docker-compose file contains settings for service deployment and operation?

• A. VOLUME
• B. USER
• C. ADD
• D. CMD

Correct Answer: C

Q14)Which section of the docker-compose file contains settings for service deployment and operation when deployed to a Docker swarm?

• A. services
• B. build
• C. deploy
• D. args

Correct Answer: C

Q15)Which high-level language is used to define plays, tasks, and playbooks is fully supported by Ansible.

• A. YAML
• B. Python
• C. XML
• D. JSON

Correct Answer: A

Q16)What does it mean if all of the EBS volumes connected to an active EC2 instance have 0 IOPS and a full I/O queue?

• A. The I/O queue is flushing its buffers.
• B. The disc head(s) of your EBS are looking for magnetic stripes.
• C. There is no access to the EBS volume.
• D. The EBS volume needs to be re-mounted in the OS.

Correct Answer: C

Q17)What would be the best method for leveraging distribution-specific commands to run a single playbook across many Linux distributions?

• A. Make fact-finding possible and adapt the distribution to the task by using the “when” conditional.
• B. This is not feasible; a different playbook is needed for each target Linux distribution.
• C. In the tasks, use “ignore errors: true.”
• D. Create your own checks for each command that is executed using the “shell” module.

Correct Answer: A

Q18)Which AWS CloudFormation status represents a failure state?

• A. <code>UPDATE_COMPLETE_CLEANUP_IN_PROGRESS</code>
• B. <code>DELETE_COMPLETE_WITH_ARTIFACTS</code>
• C. <code>ROLLBACK_IN_PROGRESS</code>
• D. <code>ROLLBACK_FAILED</code>

Correct Answer: D

Q19)Which of the following claims regarding the design of AWS Elastic Beanstalk is true?

• A. Applications are deployed into a variety of contexts.
• B. Applications and environments both have numerous deployments.
• C. Environments and applications both have a wide variety of deployments.
• D. Environments have multiple applications, and deployments have several environments.

Correct Answer: C

Q20)What characteristics of DynamoDB’s Global Secondary Key exist?

• A. The partition key and sort key may not match the table exactly.
• B. The only key that can differ from the table is the partition key.
• C. Only one of the keys—the partition key or the sort key—can be distinct from the table.
• D. The table can only differ from the sort key.

Correct Answer: A

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