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Amazon Web Services Data-Engineer-Associate Practice Test Questions Answers

Exam Code: Data-Engineer-Associate (Updated 302 Q&As with Explanation)
Exam Name: AWS Certified Data Engineer - Associate (DEA-C01)
Last Update: 26-Jul-2026
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Questions Include:

  • Single Choice: 263 Q&A's
  • Multiple Choice: 39 Q&A's

  • Data-Engineer-Associate Overview

    Amazon Web Services Data-Engineer-Associate Exam Overview

    Aspect Details
    Exam Name AWS Certified Data Engineer – Associate Exam
    Certification AWS Certified Data Engineer – Associate
    Duration 180 minutes
    Number of Questions Approximately 65 questions
    Exam Format Multiple choice, multiple answers, and scenario-based questions
    Passing Score 70% or higher
    Language English, Japanese, Korean, and Simplified Chinese
    Exam Mode Online Proctored or In-Person Proctored
    Prerequisites 1. Understanding of AWS services
    2. Hands-on experience with data lakes, ETL, and data pipelines
    3. Knowledge of databases, storage, and big data processing tools
    Topics Covered 1. Data Collection
    2. Data Storage and Management
    3. Data Processing
    4. Data Security and Compliance
    5. Data Analytics and Reporting
    6. Data Engineering on AWS Platform
    Preparation Resources 1. AWS Training and Certification Courses
    2. AWS Whitepapers and Documentation
    3. Practice exams and sample questions
    Tools & Technologies Covered 1. AWS Glue
    2. Amazon Redshift
    3. AWS Lambda
    4. Amazon S3
    5. Amazon EMR
    6. AWS Kinesis
    7. AWS RDS, Aurora, DynamoDB
    Recommended Experience 1-2 years of hands-on experience in data engineering tasks on AWS
    Topics Breakdown (Approx. %) - Data Collection & Ingestion: 20-25%
    - Data Storage & Management: 20-25%
    - Data Processing: 25-30%
    - Data Security & Compliance: 10-15%
    - Data Analytics & Reporting: 10-15%

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    Amazon Web Services Data-Engineer-Associate Exam Topics Breakdown

    Exam Section Topics Covered Approx. Percentage
    1. Data Collection & Ingestion - Data ingestion using AWS services (Kinesis, SQS, AWS Glue)
    - Data streaming and batch ingestion
    - ETL processes
    - Data pipeline creation
    20-25%
    2. Data Storage & Management - Data storage in Amazon S3, Redshift, RDS, DynamoDB, and other storage options
    - Data lake architecture
    - Data warehousing and management
    20-25%
    3. Data Processing - Using AWS Glue, Lambda, EMR, and Kinesis for processing data
    - Transforming and cleansing data
    - Data wrangling and pipeline management
    25-30%
    4. Data Security & Compliance - Ensuring data encryption in transit and at rest
    - Access control policies (IAM, AWS KMS)
    - GDPR and compliance requirements
    10-15%
    5. Data Analytics & Reporting - Data analytics services like AWS Athena, Redshift, and QuickSight
    - Creating and managing data reports and dashboards
    10-15%

     

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    The Amazon Web Services Data-Engineer-Associate exam validates your ability to design, develop, and deploy data pipelines on the AWS platform. It assesses your understanding of ingesting, transforming, and storing data using various AWS services like S3, Redshift, Glue, and EMR.

    There are several benefits to becoming Amazon Web Services Data-Engineer-Associate exam, including:

    • Increased knowledge and credibility in building data solutions on AWS.
    • Enhanced job prospects in big data and data engineering roles requiring AWS expertise.
    • Demonstrated ability to design and manage scalable data pipelines for various use cases.
    • Stronger foundation for pursuing more advanced AWS certifications in data specialties.

    There are no particular requisites for taking this exam. Only the exam candidates should have required knowledge on the content of the Amazon Web Services Data-Engineer-Associate exam syllabus. They should also develop their hand-on exposure on the all topics.

    The Amazon Web Services Data-Engineer-Associate exam covers a broad range of AWS functionalities relevant to data engineering, including:

    • Designing and implementing data pipelines using AWS services
    • Selecting appropriate data storage solutions (S3, Redshift, DynamoDB, etc.)
    • Data transformation and processing with services like Glue, EMR, and Lambda
    • Designing for scalability, fault tolerance, and cost-effectiveness
    • Data security and encryption best practices
    • Monitoring and troubleshooting data pipelines

    The Amazon Web Services Data-Engineer-Associate exam is a computer-based test consisting of multiple-choice questions with a single best answer. You will be given a specific time limit to complete the exam.

    The Amazon Web Services Data-Engineer-Associate certification does not have an expiration date. However, the AWS cloud platform and data engineering practices are constantly evolving, so staying updated is recommended.

    The current cost of the Amazon Web Services Data-Engineer-Associate exam is $150 USD.

    Solution2Pass offers the best support to its clients for Amazon Web Services Data-Engineer-Associate exam preparation. The clients can contact our Live Chat facility or Customer Support Service to get immediate help on any issue regarding certification syllabus.

    Data-Engineer-Associate Questions and Answers

    Question # 1

    A company has a gaming application that stores data in Amazon DynamoDB tables. A data engineer needs to ingest the game data into an Amazon OpenSearch Service cluster. Data updates must occur in near real time.

    Which solution will meet these requirements?

    A.

    Use AWS Step Functions to periodically export data from the Amazon DynamoDB tables to an Amazon S3 bucket. Use an AWS Lambda function to load the data into Amazon OpenSearch Service.

    B.

    Configure an AW5 Glue job to have a source of Amazon DynamoDB and a destination of Amazon OpenSearch Service to transfer data in near real time.

    C.

    Use Amazon DynamoDB Streams to capture table changes. Use an AWS Lambda function to process and update the data in Amazon OpenSearch Service.

    D.

    Use a custom OpenSearch plugin to sync data from the Amazon DynamoDB tables.

    Question # 2

    A company uses an organization in AWS Organizations to manage multiple AWS accounts. The company uses an enhanced fanout data stream in Amazon Kinesis Data Streams to receive streaming data from multiple producers. The data stream runs in Account A. The company wants to use an AWS Lambda function in Account B to process the data from the stream. The company creates a Lambda execution role in Account B that has permissions to access data from the stream in Account A.

    What additional step must the company take to meet this requirement?

    A.

    Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account A.

    B.

    Add a resource-based policy to the data stream to allow read access for the cross-account Lambda execution role.

    C.

    Create a service control policy (SCP) to grant the data stream read access to the cross-account Lambda execution role. Attach the SCP to Account B.

    D.

    Add a resource-based policy to the cross-account Lambda function to grant the data stream read access to the function.

    Question # 3

    A data engineer needs to create a new empty table in Amazon Athena that has the same schema as an existing table named old-table.

    Which SQL statement should the data engineer use to meet this requirement?

    A.

    B.
    C.
    D.
    Question # 4

    A retail company stores order information in an Amazon Aurora table named Orders. The company needs to create operational reports from the Orders table with minimal latency. The Orders table contains billions of rows, and over 100,000 transactions can occur each second.

    A marketing team needs to join the Orders data with an Amazon Redshift table named Campaigns in the marketing team ' s data warehouse. The operational Aurora database must not be affected.

    Which solution will meet these requirements with the LEAST operational effort?

    A.

    Use AW5 Database Migration Service (AWS DMS) Serverless to replicate the Orders table to Amazon Redshift. Create a materialized view in Amazon Redshift to join with the Campaigns table.

    B.

    Use the Aurora zero-ETL integration with Amazon Redshift to replicate the Orders table. Create a materialized view in Amazon Redshift to join with the Campaigns table.

    C.

    Use AWS Glue to replicate the Orders table to Amazon Redshift. Create a materialized view in Amazon Redshift to join with the Campaigns table.

    D.

    Use federated queries to query the Orders table directly from Aurora. Create a materialized view in Amazon Redshift to join with the Campaigns table.

    Question # 5

    A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options.

    The company ' s current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS.

    Which extract, transform, and load (ETL) service will meet these requirements?

    A.

    AWS Glue

    B.

    Amazon EMR

    C.

    AWS Lambda

    D.

    Amazon Redshift

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