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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:
302
Last Updated:
Oct 27, 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 generates reports from 30 tables in an Amazon Redshift data warehouse. The data source is an operational Amazon Aurora MySQL database that contains 100 tables. Currently, the company refreshes all data from Aurora to Redshift every hour, which causes delays in report generation.

Which combination of steps will meet these requirements with the LEAST operational overhead? (Select TWO.)

Options:

A.

Use AWS Database Migration Service (AWS DMS) to create a replication task. Select only the required tables.

B.

Create a database in Amazon Redshift that uses the integration.

C.

Create a zero-ETL integration in Amazon Aurora. Select only the required tables.

D.

Use query editor v2 in Amazon Redshift to access the data in Aurora.

E.

Create an AWS Glue job to transfer each required table. Run an AWS Glue workflow to initiate the jobs every 5 minutes.

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Question 2

A company wants to use Apache Spark jobs that run on an Amazon EMR cluster to process streaming data. The Spark jobs will transform and store the data in an Amazon S3 bucket. The company will use Amazon Athena to perform analysis.

The company needs to optimize the data format for analytical queries.

Which solutions will meet these requirements with the SHORTEST query times? (Select TWO.)

Options:

A.

Use Avro format. Use AWS Glue Data Catalog to track schema changes.

B.

Use ORC format. Use AWS Glue Data Catalog to track schema changes.

C.

Use Apache Parquet format. Use an external Amazon DynamoDB table to track schema changes.

D.

Use Apache Parquet format. Use AWS Glue Data Catalog to track schema changes.

E.

Use ORC format. Store schema definitions in separate files in Amazon S3.

Question 3

A manufacturing company uses AWS Glue jobs to process IoT sensor data to generate predictive maintenance models. A data engineer needs to implement automated data quality checks to identify temperature readings that are outside the expected range of -50°C to 150°C. The data quality checks must also identify records that are missing timestamp values.

The data engineer needs a solution that requires minimal coding and can automatically flag the specified issues.

Which solution will meet these requirements?

Options:

A.

Create an AWS Glue DataBrew project to profile the sensor data. Define completeness rules for timestamps. Set up numeric range validation for temperature values.

B.

Use AWS Glue ' s Data Quality rules and machine learning (ML)-based anomaly detection to identify missing timestamps and to detect temperature anomalies.

C.

Create an AWS Lambda function to scan the sensor data files to validate temperature ranges. Use AWS Glue Data Catalog tables to check timestamp completeness.

D.

Create an AWS Glue DynamicFrame that uses a custom data quality operator to profile the sensor data. Use Amazon SageMaker Data Wrangler transforms to validate timestamps and temperature ranges.

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