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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)
Questions:
218
Last Updated:
Dec 28, 2025
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

Question 1

A company plans to use Amazon Kinesis Data Firehose to store data in Amazon S3. The source data consists of 2 MB csv files. The company must convert the .csv files to JSON format. The company must store the files in Apache Parquet format.

Which solution will meet these requirements with the LEAST development effort?

Options:

A.

Use Kinesis Data Firehose to convert the csv files to JSON. Use an AWS Lambda function to store the files in Parquet format.

B.

Use Kinesis Data Firehose to convert the csv files to JSON and to store the files in Parquet format.

C.

Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON and stores the files in Parquet format.

D.

Use Kinesis Data Firehose to invoke an AWS Lambda function that transforms the .csv files to JSON. Use Kinesis Data Firehose to store the files in Parquet format.

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Question 2

A data engineer uses Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to run data pipelines in an AWS account. A workflow recently failed to run. The data engineer needs to use Apache Airflow logs to diagnose the failure of the workflow. Which log type should the data engineer use to diagnose the cause of the failure?

Options:

A.

YourEnvironmentName-WebServer

B.

YourEnvironmentName-Scheduler

C.

YourEnvironmentName-DAGProcessing

D.

YourEnvironmentName-Task

Question 3

A company wants to combine data from multiple software as a service (SaaS) applications for analysis.

A data engineering team needs to use Amazon QuickSight to perform the analysis and build dashboards. A data engineer needs to extract the data from the SaaS applications and make the data available for QuickSight queries.

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

Options:

A.

Create AWS Lambda functions that call the required APIs to extract the data from the applications. Store the data in an Amazon S3 bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight

B.

Use AWS Lambda functions as Amazon Athena data source connectors to run federated queries against the SaaS applications. Create an Athena data source and a dataset in QuickSight.

C.

Use Amazon AppFlow to create a Row for each SaaS application. Set an Amazon S3 bucket as the destination. Schedule the flows to extract the data to the bucket. Use AWS Glue to catalog the data in the S3 bucket. Create a data source and a dataset in QuickSight.

D.

Export data the from the SaaS applications as Microsoft Excel files. Create a data source and a dataset in QuickSight by uploading the Excel files.