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Amazon Web Services Data-Engineer-Associate Dumps Questions Answers

Data-Engineer-Associate exam

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AWS Certified Data Engineer - Associate (DEA-C01)

Last Update Jan 22, 2026
Total Questions : 231 With Comprehensive Analysis

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Last Update Jan 22, 2026
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Amazon Web Services Data-Engineer-Associate Exam Dumps FAQs

The Data-Engineer-Associate AWS Certified Associate exam consists of multiple-choice and multiple-response questions. The exam duration is 130 minutes, and the passing score is 720 out of 1000.
The Data-Engineer-Associate AWS Certified Associate exam covers data storage, processing, security, and analysis using AWS tools. It also tests your ability to design, optimize, and automate data workflows.
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The Amazon Web Services Data-Engineer-Associate (D-Associate) and the Amazon Web Services Machine Learning Engineer - Associate (MLA-C01) exams are both designed for professionals working with AWS, but they focus on different areas of expertise:

  • Amazon Web Services Data-Engineer-Associate Exam: The Amazon Web Services Data-Engineer-Associate Exam focuses on designing, building, and maintaining data solutions using AWS services. It covers data storage, data processing, data lakes, data pipelines, and data migration.
  • Amazon Web Services MLA-C01 Exam: The Amazon Web Services MLA-C01 Exam emphasizes implementing and operationalizing machine learning workloads on AWS. It covers data preparation, model development, deployment, orchestration of ML workflows, and monitoring ML solutions.
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AWS Certified Data Engineer - Associate (DEA-C01) Questions and Answers

Questions 1

A data engineer needs to build an extract, transform, and load (ETL) job. The ETL job will process daily incoming .csv files that users upload to an Amazon S3 bucket. The size of each S3 object is less than 100 MB.

Which solution will meet these requirements MOST cost-effectively?

Options:

A.

Write a custom Python application. Host the application on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.

B.

Write a PySpark ETL script. Host the script on an Amazon EMR cluster.

C.

Write an AWS Glue PySpark job. Use Apache Spark to transform the data.

D.

Write an AWS Glue Python shell job. Use pandas to transform the data.

Questions 2

A data engineer is using an Apache Iceberg framework to build a data lake that contains 100 TB of data. The data engineer wants to run AWS Glue Apache Spark Jobs that use the Iceberg framework.

What combination of steps will meet these requirements? (Select TWO.)

Options:

A.

Create a key named -conf for an AWS Glue job. Set Iceberg as a value for the --datalake-formats job parameter.

B.

Specify the path to a specific version of Iceberg by using the --extra-Jars job parameter. Set Iceberg as a value for the ~ datalake-formats job parameter.

C.

Set Iceberg as a value for the -datalake-formats job parameter.

D.

Set the -enable-auto-scaling parameter to true.

E.

Add the -job-bookmark-option: job-bookmark-enable parameter to an AWS Glue job.

Questions 3

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?

Options:

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.

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