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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 1, 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 is developing machine learning (ML) models. A data engineer needs to apply data quality rules to training data. The company stores the training data in an Amazon S3 bucket.

Options:

A.

Create an AWS Lambda function to check data quality and to raise exceptions in the code.

B.

Create an AWS Glue DataBrew project for the data in the S3 bucket. Create a ruleset for the data quality rules. Create a profile job to run the data quality rules. Use Amazon EventBridge to run the profile job when data is added to the S3 bucket.

C.

Create an Amazon EMR provisioned cluster. Add a Python data quality package.

D.

Create AWS Lambda functions to evaluate data quality rules and orchestrate with AWS Step Functions.

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Question 2

An ecommerce company stores sales data in an AWS Glue table named sales_data. The company stores the sales_data table in an Amazon S3 Standard bucket. The table contains columns named order_id, customer_id, product_id, order_date, shipping_date, and order_amount.

The company wants to improve query performance by partitioning the sales_data table by order_date. The company needs to add the partition to the existing sales_data table in AWS Glue.

Which solution will meet these requirements?

Options:

A.

Update the AWS Glue table’s schema to include the new partition.

B.

Edit the AWS Glue table’s metadata file directly in Amazon S3.

C.

Use the AWS Glue Data Catalog API to add the new partition to the table.

D.

Manually modify the S3 bucket to use the new partition.

Question 3

A company builds a new data pipeline to process data for business intelligence reports. Users have noticed that data is missing from the reports.

A data engineer needs to add a data quality check for columns that contain null values and for referential integrity at a stage before the data is added to storage.

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

Options:

A.

Use Amazon SageMaker Data Wrangler to create a Data Quality and Insights report.

B.

Use AWS Glue ETL jobs to perform a data quality evaluation transform on the data. Use an IsComplete rule on the requested columns. Use a ReferentialIntegrity rule for each join.

C.

Use AWS Glue ETL jobs to perform a SQL transform on the data to determine whether requested columns contain null values. Use a second SQL transform to check referential integrity.

D.

Use Amazon SageMaker Data Wrangler and a custom Python transform to create custom rules to check for null values and referential integrity.

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