AWS Certified Data Engineer - Associate Practice Exam
AWS Certified Data Engineer - Associate Practice Exam
About AWS Certified Data Engineer - Associate Practice Exam
The AWS Certified Data Engineer - Associate has been developed to evaluate the skills and knowledge of the candidate in data-related AWS services. The certification exam focuses on developing the ability on -
- implementing data pipelines, monitor and troubleshoot issues
- Optimizing cost and performance with reference to the best practices.
Candidate planning on using AWS technology for transforming data for analysis and actionable insights, the exam offers the opportunity to excel in this domain.
Skills and Knowledge Evaluated
The AWS Certified Data Engineer - Associate (DEA-C01) exam has been developed to validate the ability of the candidate for implementing data pipelines and to monitoring, troubleshooting, and optimizing cost and performance issues with reference to the best practices. The certification exam validates a candidate’s skills to complete the given tasks including -
- Ability to transforming data, and orchestrating data pipelines while applying programming concepts.
- Ability to select an optimal data store, design data models, catalog data schemas, and managing data lifecycles.
- Ability to operationalizing, maintaining, and monitoring data pipelines. Analyze data and ensure data quality.
- Ability on implementing suitable authentication, authorization, data encryption, privacy, and governance. Enable logging.
Who should take the AWS Certified Data Engineer - Associate Exam?
The AWS Certified Data Engineer - Associate Exam has been developed for candidates having 2–3 years of experience in data engineering.
- Candidate should have the skills to understand the effects of volume, variety, and velocity on data ingestion, transformation, modeling, security, governance, privacy, schema design, and optimal data store design.
- Also, the candidate should have at least 1–2 years of hands-on experience with AWS services.
Required IT knowledge
The candidate planning to the take the exam are suggested to have general IT knowledge including -
- Setting up and performing maintenance of extract, transform, and load (ETL) pipelines from ingestion to destination
- Ability to apply high-level but language-agnostic programming concepts as required by the pipeline
- Skills to use Git commands for source control
- Ability to use data lakes to store data
- Knowledge of general concepts for networking, storage, and compute
Required AWS knowledge
The candidate are suggested to have the following AWS knowledge including -
- Knowledge to use AWS services to complete the tasks listed in the Introduction section of this exam guide
- Understanding of the AWS services for encryption, governance, protection, and logging of all data that is part of data pipelines
- Skills to compare AWS services to understand the cost, performance, and functional differences between services
- Ability to structure SQL queries and how to run SQL queries on AWS services
- Understanding of how to analyze data, verify data quality, and ensure data consistency by using AWS services
Exam Details
- Exam Name: AWS Certified Data Engineer - Associate Practice Exam
- Exam Code: DEA-C01
- Type of Questions: Multiple Choice and Multiple response Questions
- Total questions: 85 Questions
- Exam Duration: 170 minutes
- Passing Score: 720 (on a scale of 100-1000)
Course Outline
The AWS Certified Data Engineer - Associate Practice Exam covers the following topics including -
Module 1: Describe Data Ingestion and Transformation (34%)
1.1: Explain Perform data ingestion.
Candidate are required to have -
- Knowledge of throughput and latency characteristics for AWS services for ingesting data
- Understanding data ingestion patterns (including , frequency and data history)
- Ability to stream data ingestion
- Skills to perform Batch data ingestion (including, scheduled ingestion, event-driven ingestion)
- Overview of Replayability of data ingestion pipelines
- Understanding of Stateful and stateless data transactions
Develop Skills
- To read data from streaming sources
- To read data from batch sources
- To implement appropriate configuration options for batch ingestion
- To consume data APIs Setting up schedulers using Amazon EventBridge, Apache Airflow, or time-based schedules for jobs and crawlers
- To set up event triggers
- To call up a Lambda function from Amazon Kinesis
- To create allowlists for IP addresses to allow connections to data sources
- To implement throttling and overcoming rate limits (for example, DynamoDB, Amazon RDS, Kinesis)
- To manage fan-in and fan-out for streaming data distribution
1.2: Explain Transform and process data.
Candidate are required to have -
- Knowledge of creating ETL pipelines based on business requirements
- Understanding of volume, velocity, and variety of data (including, structured data, unstructured data)
- Knowledge of cloud computing and distributed computing
- Ability to use Apache Spark to process data
- Understanding of intermediate data staging locations
Develop Skills
- To optimize container usage for performance needs
- To connect to different data sources
- To integrate data from multiple sources
- To optimize costs while processing data
- To implement data transformation services based on requirements
- To transform data between formats
- To troubleshoot and debug common transformation failures and performance issues
- To create data APIs to make data available to other systems by using AWS services
1.3: Explain Orchestrate data pipelines
Candidate are required to have Knowledge of -
- Integrating various AWS services to create ETL pipelines
- Managing Event-driven architecture
- Configuring AWS services for data pipelines based on schedules or dependencies
- Managing Serverless workflows
Develop Skills
- To use orchestration services to build workflows for data ETL pipelines
- To develop data pipelines for performance, availability, scalability, resiliency, and fault tolerance
- To implement and maintaining serverless workflows
- To use notification services to send alerts
1.4: Explain and apply programming concepts.
Candidates are required to have knowledge to -
- Perform continuous integration and continuous delivery (CI/CD)
- Manage SQL queries (related to data source queries and data transformations)
- Infrastructure as code (IaC) for repeatable deployments
- Managing Distributed computing\
- Handling Data structures and algorithms
- Optimizing SQL query
Develop Skills
- To optimize code to reduce runtime for data ingestion and transformation
- To configure Lambda functions to meet concurrency and performance needs
- To perform SQL queries to transform data (for example, Amazon Redshift stored procedures)
- To structure SQL queries to meet data pipeline requirements
- To useGit commands to perform actions such as creating, updating, cloning, and branching repositories
- To use the AWS Serverless Application Model (AWS SAM) to package and deploy serverless data pipelines
- To use and mount storage volumes from within Lambda functions
Module 2: Describe Data Store Management (26%)
2.1: Explain Choose a data store.
Candidate should have -
- Knowledge of storage platforms and its features
- Knowledge of storage services and configuring specific performance demands
- Understanding of data storage formats (including, .csv, .txt, Parquet)
- Ability to align data storage with data migration requirements
- Skills to determining the appropriate storage solution for specific access patterns
- Skills to manage locks to prevent access to data
Develop Skills
- To implement the suitable storage services for specific cost and performance requirements
- To configure the appropriate storage services for specific access patterns and requirements
- To applyi storage services to appropriate use cases
- To integrate migration tools into data processing systems
- To implement data migration or remote access methods
2.2: Explain Data Cataloging Systems
Candidates are required to have -
- Knowledge to create a data catalog
- Skills to classify data based on requirements
- Knowledge of components of metadata and data catalogs
Build Skills
- To use data catalogs to consume data from the data’s source
- To build and reference a data catalog
- To identify schemas and using AWS Glue crawlers to populate data catalogs
- To synchronize partitions with a data catalog
- To create new source or target connections for cataloging
2.3: Explain and manage the lifecycle of data
Candidate should have Knowledge of -
- Suggesting suitable storage solutions to address hot and cold data requirements
- Optimizing the cost of storage based on the data lifecycle
- Deleting data to meet business and legal requirements
- Data retention policies and archiving strategies
- Protecting data with sutable resiliency and availability
Develop Skills
- To perform load and unload operations to move data between Amazon S3 and Amazon Redshift
- To manage S3 Lifecycle policies to change the storage tier of S3 data
- To expire data when it reaches a specific age by using S3 Lifecycle policies
- To manage S3 versioning and DynamoDB TTL
2.4: Explain design data models and schema evolution.
Candidate should have knowledge of -
- Concepts of Data modeling
- Ensuring accuracy and trustworthiness of data by using data lineage
- Best practices and techniques for indexing, partitioning strategies, compression, and other data optimization techniques
- Modelling structured, semi-structured, and unstructured data
- Techniques of schema evolution
Build Skills in
- To design schemas for Amazon Redshift, DynamoDB, and Lake Formation
- To address changes to the characteristics of data
- To perform schema conversion (for example, by using the AWS Schema
- To manage conversion Tool [AWS SCT] and AWS DMS Schema Conversion)
- To establish data lineage by using AWS tools
Module 3: Describe Data Operations and Support (22%)
3.1: Explain and automate data processing by using AWS services.
Candidates should have knowledge of -
- Maintaining and troubleshooting data processing for repeatable business outcomes
- Using API calls for data processing
- Identifying services accept scripting
Build Skills
- To orchestrate data pipelines
- To troubleshoot Amazon managed workflows
- To calling SDKs to access Amazon features from code
- To use the features of AWS services to process data
- To consume and maintaining data APIs
- To prepare data transformation
- To use Lambda to automate data processing
- To manage events and schedulers
3.2: Explain and Analyze data by using AWS services.
Candidates should have Knowledge of -
- Providing tradeoffs between provisioned services and serverless services
- Running and executing SQL queries
- Visualizing data for analysis
- Applying cleansing techniques
- Data aggregation, rolling average, grouping, and pivoting
Build Skills
- To visualize data by using AWS services and tools
- To verify and clean data
- To use Athena to query data or to create views
- To use Athena notebooks that use Apache Spark to explore data
3.3: Explain the process of maintaining and monitoring data pipelines
Candidates should have knowledge of -
- Using log application data
- Performance tuning using Best practices
- Providing log access to AWS services
- Amazon Macie, AWS CloudTrail, and Amazon CloudWatch
Build Skills
- To Extract logs for audits
- To deploy, log and monitor solutions for facilitating auditing and traceability
- To use notifications during monitoring to send alerts
- To troubleshoot performance issues
- To use CloudTrail to track API calls
- To troubleshoot and maintain pipelines
- To use Amazon CloudWatch Logs for logging into the application data (with a focus on configuration and automation)
- To analyze logs with AWS services
3.4: Explain and ensure data quality
Candidates should have knowledge of -
- Implementing techniques of Data sampling
- techniques to implement data skew mechanisms
- Concepts of Data validation (data completeness, consistency, accuracy, and integrity) and Data profiling
Build Skills
- To run data quality checks while processing the data
- To define data quality rules
- To investigate data consistency
Module 4: Describe Data Security and Governance (18%)
4.1: Explain to apply authentication mechanisms.
Candidates should have knowledge of -
- Concepts including VPC security networking concepts
- Differentiating managed services and unmanaged services
- Authenticating methods (password-based, certificate-based, and role-based)
- Differentiating AWS managed policies and customer managed policies
Build Skills
- To update VPC security groups
- To create and update IAM groups, roles, endpoints, and services
- To create and rotate credentials for password management
- To set up IAM roles for access
- To apply IAM policies to roles, endpoints, and services
4.2: Explain and apply authorization mechanisms
Candidates should have knowledge of -
- Various Authorization methods (role-based, policy-based, tag-based, and attributebased)
- Principle of least privilege applicable to AWS security
- Role-based access control and expected access patterns
- • Methods of protecting data from unauthorized access across services
Build Skills
- To create custom IAM policies when a managed policy does not meet the requirements
- To store application and database credentials
- To provide database users, groups, and roles access and authority in a database
- To manage permissions through Lake Formation
4.3: Explain and ensure data encryption and masking.
Candidates should have knowledge of -
- Available Data encryption options in AWS analytics services
- Differentiating client-side encryption and server-side encryption
- Protecting sensitive data
- Data anonymization, masking, and key salting
Build Skills
- To apply data masking and anonymization according to compliance laws or company policies
- To use encryption keys to encrypt or decrypt data
- To configure encryption across AWS account boundaries
- To enable encryption in transit for data.
4.4: Explain and prepare logs for audit
Candidates should have knowledge of -
- Logging application data
- Logging access to AWS services
- Managing Centralized AWS logs
Build Skills
- To use CloudTrail to track API calls
- To use CloudWatch Logs to store application logs
- To use AWS CloudTrail Lake for centralized logging queries
- To analyze logs by using AWS services
- To integrate various AWS services to perform logging
4.5: Explain data privacy and governance
Candidates should have Knowledge of -
- Protecting personally identifiable information (PII)
- Managing Data sovereignty
Build Skills
- To grant permissions for data sharing
- To implement PII identification
- To implement data privacy strategies for preventing backups or replications of data to disallowed AWS Regions
- To manage configuration changes that occurred in an account
What do we offer?
- Full-Length Mock Test with unique questions in each test set
- Practice objective questions with section-wise scores
- In-depth and exhaustive explanation for every question
- Reliable exam reports evaluating strengths and weaknesses
- Latest Questions with an updated version
- Tips & Tricks to crack the test
- Unlimited access
What are our Practice Exams?
- Practice exams have been designed by professionals and domain experts that simulate real-time exam scenario.
- Practice exam questions have been created on the basis of content outlined in the official documentation.
- Each set in the practice exam contains unique questions built with the intent to provide real-time experience to the candidates as well as gain more confidence during exam preparation.
- Practice exams help to self-evaluate against the exam content and work towards building strength to clear the exam.
- You can also create your own practice exam based on your choice and preference
