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Amazon AWS Certified Data Engineer - Associate DEA-C01 Exam
AWS Certified Data Engineer - Associate DEA-C01 (Page 3 )

Updated On: 1-Feb-2026
Page 3 of 43
PDF with 298 Questions

A data engineer set up an AWS Lambda function to read an object that is stored in an Amazon S3 bucket. The object is encrypted by an AWS KMS key.
The data engineer configured the Lambda function’s execution role to access the S3 bucket. However, the Lambda function encountered an error and failed to retrieve the content of the object.
What is the likely cause of the error?

  1. The data engineer misconfigured the permissions of the S3 bucket. The Lambda function could not access the object.
  2. The Lambda function is using an outdated SDK version, which caused the read failure.
  3. The S3 bucket is located in a different AWS Region than the Region where the data engineer works. Latency issues caused the Lambda function to encounter an error.
  4. The Lambda function’s execution role does not have the necessary permissions to access the KMS key that can decrypt the S3 object.

Answer(s): D



A data engineer has implemented data quality rules in 1,000 AWS Glue Data Catalog tables. Because of a recent change in business requirements, the data engineer must edit the data quality rules.
How should the data engineer meet this requirement with the LEAST operational overhead?

  1. Create a pipeline in AWS Glue ETL to edit the rules for each of the 1,000 Data Catalog tables. Use an AWS Lambda function to call the corresponding AWS Glue job for each Data Catalog table.
  2. Create an AWS Lambda function that makes an API call to AWS Glue Data Quality to make the edits.
  3. Create an Amazon EMR cluster. Run a pipeline on Amazon EMR that edits the rules for each Data Catalog table. Use an AWS Lambda function to run the EMR pipeline.
  4. Use the AWS Management Console to edit the rules within the Data Catalog.

Answer(s): B



Two developers are working on separate application releases. The developers have created feature branches named Branch A and Branch B by using a GitHub repository’s master branch as the source.
The developer for Branch A deployed code to the production system. The code for Branch B will merge into a master branch in the following week’s scheduled application release.
Which command should the developer for Branch B run before the developer raises a pull request to the master branch?

  1. git diff branchB mastergit commit -m
  2. git pull master
  3. git rebase master
  4. git fetch -b master

Answer(s): C



A company stores employee data in Amazon Resdshift. A table names Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key.
Which queries will MOST increase the speed of query by using a compound sort key of the table? (Choose two.)

  1. Select *from Employee where Region ID=’North America’;
  2. Select *from Employee where Region ID=’North America’ and Department ID=20;
  3. Select *from Employee where Department ID=20 and Region ID=’North America’;
  4. Select *from Employee where Role ID=50;
  5. Select *from Employee where Region ID=’North America’ and Role ID=50;

Answer(s): B,E



A company receives test results from testing facilities that are located around the world. The company stores the test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process the files, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The data engineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and Amazon EventBridge to schedule jobs.
The company recently added more testing facilities. The time required to process files is increasing. The data engineer must reduce the data processing time.
Which solution will MOST reduce the data processing time?

  1. Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.
  2. Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.
  3. Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.
  4. Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.

Answer(s): B



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