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Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

Exam Code:
Data-Engineer-Associate
Exam Name:
AWS Certified Data Engineer - Associate (DEA-C01)
Certification:
AWS Certified Data Engineer
Vendor:
Amazon Web Services
Questions:
289
Last Updated:
Apr 23, 2026
Exam Status:
Stable
Amazon Web Services Data-Engineer-Associate

Data-Engineer-Associate: AWS Certified Data Engineer Exam 2025 Study Guide Pdf and Test Engine

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AWS Certified Data Engineer - Associate (DEA-C01) Questions and Answers

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Question 1

A company implements a data mesh that has a central governance account. The company needs to catalog all data in the governance account. The governance account uses AWS Lake Formation to centrally share data and grant access permissions.

The company has created a new data product that includes a group of Amazon Redshift Serverless tables. A data engineer needs to share the data product with a marketing team. The marketing team must have access to only a subset of columns. The data engineer needs to share the same data product with a compliance team. The compliance team must have access to a different subset of columns than the marketing team needs access to.

Which combination of steps should the data engineer take to meet these requirements? (Select TWO.)

Options:

A.

Create views of the tables that need to be shared. Include only the required columns.

B.

Create an Amazon Redshift data than that includes the tables that need to be shared.

C.

Create an Amazon Redshift managed VPC endpoint in the marketing team ' s account. Grant the marketing team access to the views.

D.

Share the Amazon Redshift data share to the Lake Formation catalog in the governance account.

E.

Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team ' s account.

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Question 2

A company uploads .csv files to an Amazon S3 bucket. The company ' s data platform team has set up an AWS Glue crawler to perform data discovery and to create the tables and schemas.

An AWS Glue job writes processed data from the tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creates the Amazon Redshift tables in the Redshift database appropriately.

If the company reruns the AWS Glue job for any reason, duplicate records are introduced into the Amazon Redshift tables. The company needs a solution that will update the Redshift tables without duplicates.

Which solution will meet these requirements?

Options:

A.

Modify the AWS Glue job to copy the rows into a staging Redshift table. Add SQL commands to update the existing rows with new values from the staging Redshift table.

B.

Modify the AWS Glue job to load the previously inserted data into a MySQL database. Perform an upsert operation in the MySQL database. Copy the results to the Amazon Redshift tables.

C.

Use Apache Spark ' s DataFrame dropDuplicates() API to eliminate duplicates. Write the data to the Redshift tables.

D.

Use the AWS Glue ResolveChoice built-in transform to select the value of the column from the most recent record.

Question 3

A company uses Amazon DataZone as a data governance and business catalog solution. The company stores data in an Amazon S3 data lake. The company uses AWS Glue with an AWS Glue Data Catalog.

A data engineer needs to publish AWS Glue Data Quality scores to the Amazon DataZone portal.

Which solution will meet this requirement?

Options:

A.

Create a data quality ruleset with Data Quality Definition Language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an Amazon Redshift data source. Enable the data quality configuration for the data source.

B.

Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.

C.

Create a data quality ruleset with Data Quality Definition Language (DQDL) rules that apply to a specific AWS Glue table. Schedule the ruleset to run daily. Configure the Amazon DataZone project to have an AWS Glue data source. Enable the data quality configuration for the data source.

D.

Configure AWS Glue ETL jobs to use an Evaluate Data Quality transform. Define a data quality ruleset inside the jobs. Configure the Amazon DataZone project to have an Amazon Redshift data source. Enable the data quality configuration for the data source.

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