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

Exam Code: Data-Engineer-Associate (Updated 218 Q&As with Explanation)
 Exam Name: AWS Certified Data Engineer - Associate (DEA-C01)
 Last Update: 13-Dec-2025
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Questions Include:

  • Single Choice: 184 Q&A's
  • Multiple Choice: 34 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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    Amazon Web Services Data-Engineer-Associate Exam Dumps FAQs

    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 needs to implement a data mesh architecture for trading, risk, and compliance teams. Each team has its own data but needs to share views. They have 1,000+ tables in 50 Glue databases. All teams use Athena and Redshift, and compliance requires full auditing and PII access control.

    A.

    Create views in Athena for on-demand analysis. Use the Athena views in Amazon Redshift to perform cross-domain analytics. Use AWS CloudTrail to audit data access. Use AWS Lake Formation to establish fine-grained access control.

    B.

    Use AWS Glue Data Catalog views. Use CloudTrail logs and Lake Formation to manage permissions.

    C.

    Use Lake Formation to set up cross-domain access to tables. Set up fine-grained access controls.

    D.

    Create materialized views and enable Amazon Redshift datashares for each domain.

    Question # 2

    A telecommunications company collects network usage data throughout each day at a rate of several thousand data points each second. The company runs an application to process the usage data in real time. The company aggregates and stores the data in an Amazon Aurora DB instance.

    Sudden drops in network usage usually indicate a network outage. The company must be able to identify sudden drops in network usage so the company can take immediate remedial actions.

    Which solution will meet this requirement with the LEAST latency?

    A.

    Create an AWS Lambda function to query Aurora for drops in network usage. Use Amazon EventBridge to automatically invoke the Lambda function every minute.

    B.

    Modify the processing application to publish the data to an Amazon Kinesis data stream. Create an Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) application to detect drops in network usage.

    C.

    Replace the Aurora database with an Amazon DynamoDB table. Create an AWS Lambda function to query the DynamoDB table for drops in network usage every minute. Use DynamoDB Accelerator (DAX) between the processing application and DynamoDB table.

    D.

    Create an AWS Lambda function within the Database Activity Streams feature of Aurora to detect drops in network usage.

    Question # 3

    A company maintains a data warehouse in an on-premises Oracle database. The company wants to build a data lake on AWS. The company wants to load data warehouse tables into Amazon S3 and synchronize the tables with incremental data that arrives from the data warehouse every day.

    Each table has a column that contains monotonically increasing values. The size of each table is less than 50 GB. The data warehouse tables are refreshed every night between 1 AM and 2 AM. A business intelligence team queries the tables between 10 AM and 8 PM every day.

    Which solution will meet these requirements in the MOST operationally efficient way?

    A.

    Use an AWS Database Migration Service (AWS DMS) full load plus CDC job to load tables that contain monotonically increasing data columns from the on-premises data warehouse to Amazon S3. Use custom logic in AWS Glue to append the daily incremental data to a full-load copy that is in Amazon S3.

    B.

    Use an AWS Glue Java Database Connectivity (JDBC) connection. Configure a job bookmark for a column that contains monotonically increasing values. Write custom logic to append the daily incremental data to a full-load copy that is in Amazon S3.

    C.

    Use an AWS Database Migration Service (AWS DMS) full load migration to load the data warehouse tables into Amazon S3 every day Overwrite the previous day's full-load copy every day.

    D.

    Use AWS Glue to load a full copy of the data warehouse tables into Amazon S3 every day. Overwrite the previous day's full-load copy every day.

    Question # 4

    A company stores customer data in an Amazon S3 bucket. The company must permanently delete all customer data that is older than 7 years.

    A.

    Configure an S3 Lifecycle policy to permanently delete objects that are older than 7 years.

    B.

    Use Amazon Athena to query the S3 bucket for objects that are older than 7 years. Configure Athena to delete the results.

    C.

    Configure an S3 Lifecycle policy to move objects that are older than 7 years to S3 Glacier Deep Archive.

    D.

    Configure an S3 Lifecycle policy to enable S3 Object Lock on all objects that are older than 7 years.

    Question # 5

    A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.

    The company stores all order data at a single path within the S3 bucket. The data has more than 100 columns. The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.

    The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded. A data engineer must resolve the performance issues for the queries.

    Which combination of steps will meet this requirement with LEAST developmental effort? (Select TWO.)

    A.

    Configure the third-party application to create the files in a columnar format.

    B.

    Develop an AWS Glue ETL job to convert the multiple daily CSV files to one file for each day.

    C.

    Partition the order data in the S3 bucket based on order date.

    D.

    Configure the third-party application to create the files in JSON format.

    E.

    Load the JSON data into the Amazon Redshift table in a SUPER type column.

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