How to pass AWS Machine Learning Specialty Exam?

The AWS Machine Learning Specialty Certification stands as a globally acknowledged credential, affirming a professional’s mastery in devising, executing, deploying, and upholding machine learning solutions on the AWS platform. This certification showcases a candidate’s adeptness in leveraging AWS services to construct, train, and launch machine learning models. By acquiring this certification, individuals elevate their credibility and prominence in the industry, while also broadening their career horizons and potential income. This blog aims to furnish prospective candidates with an exhaustive guide on how to excel in the AWS Machine Learning Specialty Exam.

The blog encompasses an exploration of the exam’s structure, prerequisites, and the subject matter that will be assessed. It further extends insights into strategies and advice for effective exam preparation, including crafting a study regimen, capitalizing on AWS learning materials, and honing skills through sample questions and simulated tests. The overarching objective of this blog is to empower candidates to triumph in the AWS Machine Learning Specialty Exam and attain this invaluable certification.

Why choose AWS Machine Learning Specialty (MLS-C01)?

There are several reasons why you may choose to pursue the AWS Machine Learning Specialty Certification (MLS-C01):

• Rising Demand for Machine Learning Experts: The realm of machine learning is experiencing rapid expansion, resulting in a strong demand for professionals possessing specialized knowledge in this domain. Earning the AWS Machine Learning Specialty Certification can set you apart in the competitive job market, significantly enhancing your prospects of securing a position within this burgeoning field.
• Validation of Proficiency and Know-How: This certification serves as an affirmation of your aptitude and mastery in constructing, training, and deploying machine learning models through the utilization of AWS services. It carries the potential to bolster your reputation and standing both within the industry and among your peers.
• AWS: A Premier Cloud Provider: As one of the world’s premier cloud service providers, AWS delivers an array of highly scalable, dependable, and cost-efficient machine learning services. Acquiring the AWS Machine Learning Specialty Certification allows you to showcase your expertise in utilizing these services, thereby empowering your organization to harness the capabilities of AWS for machine learning initiatives.
• Stepping Stone for Career Progression: This certification acts as a gateway to fresh career prospects and propels your advancement within the professional landscape. It can facilitate your transition to more senior positions, such as machine learning engineer, data scientist, or AI architect, consequently amplifying your potential for increased earnings.

AWS Machine Learning Specialty Exam Structure

The AWS Machine Learning Specialty exam is designed for individuals working as data scientists and those engaged in development roles. This certification has been meticulously crafted to authenticate your expertise in conceptualizing, implementing, sustaining, and launching machine learning (ML) solutions tailored for business applications. By obtaining this certification, developers can exhibit their proficiency in algorithmically identifying patterns and demonstrate their adeptness in executing or devising workloads within the AWS cloud environment.

AWS Machine Learning Specialty Exam Details

AWS Machine Learning Certification Questions are in Multiple Choice format. Moreover, you get only 170 minutes to complete the exam. Also, AWS Machine Learning Certification Cost is USD $ 300. You can take the exam in various languages including English, Japanese, Korean, and Simplified Chinese.

Exam Code	MLS-C01
Exam Type	Specialty
Exam Duration	170 minutes
Exam Cost	USD $ 300
Exam Format	Multiple-choice Questions and Multiple-response Questions
Exam Scoring	Scaled score from 100 to 1000
Passing Score	750
Exam Language	English, Japanese, Korean, and Simplified Chinese

AWS Machine Learning Certification Prerequisites

In order to qualify for the AWS Machine Learning Specialty Certification, you must fulfill the following requirements:

1. Possess an AWS Certified Cloud Practitioner credential or a current Associate-level certification in any AWS Certification path.
2. Have at least 2 years of practical experience in the development, training, and deployment of machine learning models within the AWS Cloud environment.
3. Exhibit proficiency with popular machine learning frameworks like TensorFlow, PyTorch, or Apache MXNet.
4. Demonstrate familiarity with AWS machine learning services, including Amazon SageMaker, Amazon Rekognition, Amazon Comprehend, and Amazon Translate.

Beyond these prerequisites, it is advisable to possess a solid grasp of fundamental statistics, data modeling, and concepts related to software development.

It’s important to note that meeting the eligibility criteria alone does not guarantee success in the exam. You will need to thoroughly prepare for the exam by studying the exam guide and taking practice exams to ensure that you have the knowledge and skills needed to pass the exam.

AWS Machine Learning Certification Course

There are 4 domains to focus on in this AWS Machine Learning Specialty Certificate,

Domain 1: Data Engineering (20%)

1.1 Create data repositories for ML.

• Identify data sources (e.g., content and location, primary sources such as user data) (AWS Documentation: Supported data sources)
• Determine storage mediums (for example, databases, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon Elastic Block Store [Amazon EBS]). (AWS Documentation: Using Amazon S3 with Amazon ML, Creating a Datasource with Amazon Redshift Data, Using Data from an Amazon RDS Database, Host instance storage volumes, Amazon Machine Learning and Amazon Elastic File System)

1.2 Identify and implement a data ingestion solution.

• Identify data job styles and job types (for example, batch load, streaming).
• Orchestrate data ingestion pipelines (batch-based ML workloads and streaming-based ML workloads).
  • Amazon Kinesis (AWS Documentation: Amazon Kinesis Data Streams)
  • Amazon Data Firehose
  • Amazon EMR (AWS Documentation: Process Data Using Amazon EMR with Hadoop Streaming, Optimize downstream data processing)
  • Amazon Glue (AWS Documentation: Simplify data pipelines, AWS Glue)
  • Amazon Managed Service for Apache Flink
• Schedule Job (AWS Documentation: Job scheduling, Time-based schedules for jobs and crawlers)

1.3 Identify and implement a data transformation solution.

• Transforming data transit (ETL: Glue, Amazon EMR, AWS Batch) (AWS Documentation: extract, transform, and load data for analytic processing using AWS Glue)
• Handle ML-specific data by using MapReduce (for example, Apache Hadoop, Apache Spark, Apache Hive). (AWS Documentation: Large-Scale Machine Learning with Spark on Amazon EMR, Apache Hive on Amazon EMR, Apache Spark on Amazon EMR, Use Apache Spark with Amazon SageMaker, Perform interactive data engineering and data science workflows)

Domain 2: Exploratory Data Analysis (24%)

2.1 Sanitize and prepare data for modeling.

• Identify and handle missing data, corrupt data, stop words, etc. (AWS Documentation: Managing missing values in your target and related datasets, Amazon SageMaker DeepAR now supports missing values, Configuring Text Analysis Schemes)
• Formatting, normalizing, augmenting, and scaling data (AWS Documentation: Understanding the Data Format for Amazon ML, Common Data Formats for Training, Data Transformations Reference, AWS Glue DataBrew, Easily train models using datasets, Visualizing Amazon SageMaker machine learning predictions)
• Determine whether there is sufficient labeled data. (AWS Documentation:data labeling for machine learning, Amazon Mechanical Turk, Use Amazon Mechanical Turk with Amazon SageMaker)
  • Identify mitigation strategies.
  • Use data labelling tools (for example, Amazon Mechanical Turk).

2.2 Perform feature engineering.

• Identify and extract features from data sets, including from data sources such as text, speech, image, public datasets, etc. (AWS Documentation: Feature Processing, Feature engineering, Amazon Textract, Amazon Textract features)
• Analyze/evaluate feature engineering concepts (binning, tokenization, outliers, synthetic features, 1 hot encoding, reducing dimensionality of data) (AWS Documentation: Data Transformations Reference, Building a serverless tokenization solution to mask sensitive data, ML-powered anomaly detection for outliers, ONE_HOT_ENCODING, Running Principal Component Analysis, Perform a large-scale principal component analysis)

2.3 Analyze and visualize data for ML.

• Create Graphs (scatter plot, time series, histogram, box plot) (AWS Documentation: Using scatter plots, Run a query that produces a time series visualization, Using histograms, Using box plots)
• Interpreting descriptive statistics (correlation, summary statistics, p value)
• Perform cluster analysis (for example, hierarchical, diagnosis, elbow plot, cluster size).

Domain 3: Modeling (36%)

3.1 Frame business problems as ML problems.

• Determine when to use and when not to use ML (AWS Documentation: When to Use Machine Learning)
• Know the difference between supervised and unsupervised learning
• Select from among classification, regression, forecasting, clustering, recommendation, and foundation models. (AWS Documentation: K-means clustering with Amazon SageMaker, Building a customized recommender system in Amazon SageMaker)

3.2 Select the appropriate model(s) for a given ML problem.

• Xgboost, logistic regression, K-means, linear regression, decision trees, random forests, RNN, CNN, Ensemble, Transfer learning (AWS Documentation: XGBoost Algorithm, K-means clustering with Amazon SageMaker, Forecasting financial time series, Amazon Forecast can now use Convolutional Neural Networks, Detecting hidden but non-trivial problems in transfer learning models)
• Express intuition behind models

3.3 Train ML models.

• Split data between training and validation (for example, cross validation). (AWS Documentation: Train a Model, Incremental Training, Managed Spot Training, Validate a Machine Learning Model, Cross-Validation, Model support, metrics, and validation, Splitting Your Data)
• Understand optimization techniques for ML training (for example, gradient descent, loss functions, convergence).
• Choose appropriate compute resources (for example GPU or CPU, distributed or non-distributed).
  • Choose appropriate compute platforms (Spark or non-Spark).
• Update and retraining Models (AWS Documentation:Retraining Models on New Data, Automating model retraining and deployment)
  • Batch vs. real-time/online

3.4 Perform hyperparameter optimization.

• Perform Regularization (AWS Documentation:Training Parameters)
  • Drop out
  • L1/L2
• Perform Cross validation (AWS Documentation: Cross-Validation)
• Model initialization
• Neural network architecture (layers/nodes), learning rate, activation functions
• Understand tree-based models (number of trees, number of levels).
• Understand linear models (learning rate).

3.5 Evaluate ML models.

• Avoid overfitting and underfitting
  • Detect and handle bias and variance (AWS Documentation: Underfitting vs. Overfitting, Amazon SageMaker Clarify Detects Bias and Increases the Transparency, Amazon SageMaker Clarify)
• Evaluate metrics (for example, area under curve [AUC]-receiver operating characteristics [ROC], accuracy, precision, recall, Root Mean Square Error [RMSE], F1 score).
• Interpret confusion matrix (AWS Documentation: Custom classifier metrics)
• Offline and online model evaluation (A/B testing) (AWS Documentation: Validate a Machine Learning Model, Machine Learning Lens)
• Compare models using metrics (time to train a model, quality of model, engineering costs) (AWS Documentation: Easily monitor and visualize metrics while training models, Model Quality Metrics, Monitor model quality)
• Cross validation (AWS Documentation: Cross-Validation, Model support, metrics, and validation)

Domain 4: Machine Learning Implementation and Operations (20%)

4.1 Build ML solutions for performance, availability, scalability, resiliency, and fault tolerance. (AWS Documentation: Review the ML Model’s Predictive Performance, Best practices, Resilience in Amazon SageMaker)

• Log and monitor AWS environments (AWS Documentation:Logging and Monitoring)
  • AWS CloudTrail and AWS CloudWatch (AWS Documentation: Logging Amazon ML API Calls with AWS CloudTrail, Log Amazon SageMaker API Calls, Monitoring Amazon ML, Monitor Amazon SageMaker)
  • Build error monitoring solutions (AWS Documentation: ML Platform Monitoring)
• Deploy to multiple AWS Regions and multiple Availability Zones. (AWS Documentation: Regions and Endpoints, Best practices)
• AMI and golden image (AWS Documentation: AWS Deep Learning AMI)
• Docker containers (AWS Documentation: Why use Docker containers for machine learning development, Using Docker containers with SageMaker)
• Deploy Auto Scaling groups (AWS Documentation: Automatically Scale Amazon SageMaker Models, Configuring autoscaling inference endpoints)
• Rightsizing resources, for example:
  • Instances (AWS Documentation: Ensure efficient compute resources on Amazon SageMaker)
  • Provisioned IOPS (AWS Documentation: Optimizing I/O for GPU performance tuning of deep learning)
  • Volumes (AWS Documentation: Customize your notebook volume size, up to 16 TB)
• Load balancing (AWS Documentation: Managing your machine learning lifecycle)
• AWS best practices (AWS Documentation: Machine learning best practices in financial services)

4.2 Recommend and implement the appropriate ML services and features for a given problem.

• ML on AWS (application services)
  • Amazon Poly (AWS Documentation: Amazon Polly, Build a unique Brand Voice with Amazon Polly)
  • Amazon Lex (AWS Documentation: Amazon Lex, Build more effective conversations on Amazon Lex)
  • Amazon Transcribe (AWS Documentation: Amazon Transcribe, Transcribe speech to text in real time)
  • Amazon Q
• Understand AWS service quotas (AWS Documentation: Amazon SageMaker endpoints and quotas, Amazon Machine Learning endpoints and quotas, System Limits)
• Determine when to build custom models and when to use Amazon SageMaker built-in algorithms.
• Understand AWS infrastructure (for example, instance types) and cost considerations.
  • Using spot instances to train deep learning models using AWS Batch (AWS Documentation: Train Deep Learning Models on GPUs)

4.3 Apply basic AWS security practices to ML solutions.

• AWS Identity and Access Management (IAM) (AWS Documentation: Controlling Access to Amazon ML Resources, Identity and Access Management in AWS Deep Learning Containers)
• S3 bucket policies (AWS Documentation: Using Amazon S3 with Amazon ML, Granting Amazon ML Permissions to Read Your Data from Amazon S3)
• Security groups (AWS Documentation: Secure multi-account model deployment with Amazon SageMaker, Use an AWS Deep Learning AMI)
• VPC (AWS Documentation: Securing Amazon SageMaker Studio connectivity, Direct access to Amazon SageMaker notebooks, Building secure machine learning environments)
• Encryption and anonymization (AWS Documentation: Protect Data at Rest Using Encryption, Protecting Data in Transit with Encryption, Anonymize and manage data in your data lake)

4.4 Deploy and operationalize ML solutions.

• Exposing endpoints and interacting with them (AWS Documentation: Creating a machine learning-powered REST API, Call an Amazon SageMaker model endpoint)
• Understand ML models.
• A/B testing (AWS Documentation: A/B Testing ML models in production, Dynamic A/B testing for machine learning models)
• Retrain pipelines (AWS Documentation: Automating model retraining and deployment, Machine Learning Lens)
• Debug and troubleshoot ML models (AWS Documentation:Debug Your Machine Learning Models, Analyzing open-source ML pipeline models in real time, Troubleshoot Amazon SageMaker model deployments)
  • Detect and mitigate drop in performance (AWS Documentation: Identify bottlenecks, improve resource utilization, and reduce ML training costs, Optimizing I/O for GPU performance tuning of deep learning training)
  • Monitor performance of the model (AWS Documentation: Monitor models for data and model quality, bias, and explainability, Monitoring in-production ML models at large scale)

About AWS Machine Learning Specialty Exam

Exam Question types

The AWS Machine Learning Certification Exam Questions are structured in the following formats:

1. Multiple-choice questions: You will be presented with multiple options, and you are required to choose the single correct answer from the provided choices.
2. Multiple response questions: These questions entail selecting multiple correct answers from the given options.

Scoring Guide

• In Machine Learning Specialty exam you can choose one or more best suitable answers depends on the type of questions.
• In this exam no marks will be deducted on giving wrong answer.
• In the exam you can see some portion which does not have any score. It is there just for collecting general information and will not have any effect on the exam.

Result Pattern

The scoring for the AWS Machine Learning Specialty exam ranges from 100 to 1000.

To achieve a passing grade, you are required to score at least 750.

The AWS Machine Learning Specialty exam follows a pass or fail format, and your exam results will be sent to you via email within five business days from the exam date.

In this exam, you are not required to pass each individual section; rather, you need to achieve the overall passing score.

Different sections of the exam carry varying weightages, with variations in the number of questions assigned to each section.

Examination Retake Policy

AWS has specific guidelines for retaking the certification exam. According to these guidelines, you must wait for a period of 14 days before attempting the exam again. There is no predefined limit on the number of times you can take the exam; you can retake it multiple times until you successfully achieve the certification. However, it’s important to note that for each attempt, you are required to pay the full registration fee.

Registering for the exam

• You need to register first and then sign in to aws.training.
• After that, click on Certification on the top of the page.
• Then click on AWS Certification account, Schedule new exam.
• Check for the exam you want to take and click schedule at Pearson VUI button or PSI.

At the time of exam before entering the test center you are required to provide two government issued IDs with matching your name on it as on the application form.

Path for AWS Machine Learning Professionals

AWS has formulated the Machine Learning track to enable professionals to assess their competencies and practical know-how in crafting, optimizing, training, and launching machine learning models utilizing AWS cloud services. Within the realm of AWS’s Machine Learning Certification Path, two distinct paths exist.

Machine Learning Path for Data Scientist

This path is for individuals who are skilled in statistics, mathematics\s and analysis and want to become an expert in Machine learning in their organization. In this you will learn about the frameworks and analysis tools which are used for improving workplace.

Machine Learning Path for Developer

Machine Learning Developer path is for software developers and builders. This will help you learn how Artificial Intelligence and Machine learning together can help you get better partner with Data Scientist to innovating with Machine learning technologies.

Exam Preparation guide for AWS Machine Learning Specialty exam

This certification exam holds significance for individuals engaged in the roles of Data Scientist and developer. It’s imperative to adopt a determined mindset and a firm commitment to succeeding in this certification. Accomplishing this requires a well-structured study plan for the examination, alongside the acquisition of practical experience and hands-on proficiency in Machine learning within the AWS cloud environment. Beyond everything, attaining this certification will unlock numerous fresh prospects, enhancing your skill set and elevating your expertise. To provide assistance, we offer the AWS Machine Learning Specialty Study Guide.

A. Creating a Study Plan:

Before you start preparing for the AWS Machine Learning Specialty exam, it is essential to create a study plan that outlines your study goals, timelines, and resources. A study plan will help you stay focused and organized during your preparation and ensure that you cover all the topics in the exam.

B. Utilizing AWS Learning Resources:

AWS provides a range of learning resources, including whitepapers, documentation, training courses, and certification guides, to help you prepare for the AWS Machine Learning Specialty exam. Make sure to utilize these resources to gain a deep understanding of the AWS machine learning services, architectures, and deployment scenarios.

C. Practicing Sample Questions and Mock Tests:

Engaging with sample questions and participating in mock tests proves to be a valuable strategy for evaluating your grasp of the subject matter and gauging your preparedness for the exam. AWS offers an array of practice exams and sample questions, enabling you to gauge your proficiency, pinpoint areas requiring enhancement, and fine-tune your exam readiness.

D. Hands-on Experience with AWS Machine Learning Services:

Gaining practical experience with AWS machine learning services is a vital component in successfully navigating the exam. It grants you an in-depth comprehension of service operations and their application in resolving real-world challenges. It’s imperative to immerse yourself in hands-on interactions with key services like Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

E. Joining AWS Machine Learning Communities and Forums:

Engaging with AWS machine learning communities and forums can keep you well-informed about the most recent trends, updates, and optimal methods in this domain. Furthermore, it presents an exceptional platform to connect with fellow experts, fostering networking opportunities while exchanging insights and proficiencies.

F. Engaging in Peer Learning and Discussion Groups:

Participating in collaborative learning and discussion circles is another impactful approach to ready yourself for the AWS Machine Learning Specialty exam. This avenue facilitates the exchange of insights, enabling you to draw from others’ expertise and recount your own experiences. It’s a platform to address complex subjects, seek assistance with perplexing queries, and collectively enhance your understanding.

G. Seeking Professional Training and Certification Courses:

Enrolling in specialized training and certification programs can provide you with a comprehensive grasp of AWS machine learning services, optimal strategies, and practical applications. Additionally, these courses can help you pinpoint any knowledge gaps and offer the essential direction to successfully navigate the exam.

H. Familiarizing Yourself with AWS Machine Learning Use Cases and Case Studies:

Getting acquainted with real-world AWS machine learning use cases and delving into case studies can offer insights into practical service applications. This practice will aid in comprehending the optimal approaches for addressing diverse challenges through AWS machine learning services. AWS’s official documentation serves as a valuable resource for studying various sub-topics integral to the machine learning specialty certification exam. This documentation covers essential Amazon machine learning concepts like data partitioning, machine learning model types, and data manipulation. During your AWS Machine Learning Specialty preparation, the following documents are highly recommended:

• Concepts of Amazon Machine Learning
• Functionality of Machine Learning on AWS
• Machine Learning Models
• Splitting of Data
• Concept of Data Transformations

Exam Day Advice by Our Expert

Here are some exam day tips for passing the AWS Machine Learning Specialty (MLS-C01) exam:

• Be Well-Prepared and Arrive Early: Familiarize yourself with the exam location and timing, and ensure you arrive early for check-in and settling in.
• Thoroughly Understand Questions and Time Management: Dedicate ample time to comprehend each question thoroughly, ensuring you grasp the context. Effective time management is key to addressing all questions.
• Leverage Scratch Paper and Calculator: Utilize the provided scratch paper and calculator to assist in problem-solving and organizing your calculations.
• Maintain Composure and Focus: Maintain a composed and focused mindset throughout the exam. When faced with challenging questions, take a moment to breathe deeply and proceed to the next question.
• Review and Verify Answers: Prior to submission, meticulously review your answers, ensuring all questions have been addressed accurately.
• Handle Exam Stress Strategically: Implement relaxation techniques, like deep breathing or visualization, to manage any stress or anxiety that may arise during the exam.
• Adhere to Exam Guidelines: Adhere to all prescribed exam rules and guidelines to ensure an equitable and smooth exam experience.
• Take Breaks and Stay Hydrated: Take necessary breaks and stay hydrated during the exam to sustain mental clarity and physical well-being.

Final Words

• Stay committed to your study plan: Consistency is key to success, so stick to your study plan and keep working towards your goal.
• Practice, practice, practice: Practice sample questions and mock tests to get familiar with the exam format and identify areas where you need improvement.
• Utilize AWS Learning Resources: AWS provides a variety of learning resources, such as documentation, whitepapers, and training courses, to help you prepare for the exam. Make use of them!
• Don’t underestimate hands-on experience: Get hands-on experience with AWS Machine Learning services to reinforce your understanding of the concepts and principles.
• Believe in yourself: Believe that you have what it takes to pass the exam and become an AWS Machine Learning Specialist. Have confidence in your abilities and keep a positive mindset.

Remember that passing the AWS Machine Learning Specialty exam is not easy, but can be passed with the right preparation and mindset. Stay focused, work hard, and stay motivated. Good luck!