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Amazon AWS Certified Data Engineer - Associate Amazon-DEA-C01 Exam

Attempt the AWS Certified Data Engineer Associate practice test and solve real exam-like Amazon-DEA-C01 questions to prepare efficiently and increase your chances of success. Our Amazon-DEA-C01 practice questions match the actual AWS Certified Data Engineer - Associate exam format, helping you enhance confidence and improve performance. With our Amazon-DEA-C01 practice exam software, you can analyze your performance, identify weak areas, and work on them effectively to boost your final AWS Certified Data Engineer Associate exam score.

Vendor:	Amazon
Exam Name:	AWS Certified Data Engineer - Associate
Registration Code:	Amazon-DEA-C01
Related Certification:	Amazon AWS Certified Data Engineer Associate Certification
Exam Audience:	Amazon Data engineers, Database Administrators, and Cloud architects,

Total Questions

294

Last Updated

23-05-2026

Exam Duration

130 MINUTES

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Question: 1

A company wants to migrate a data warehouse from Teradata to Amazon Redshift. Which solution will meet this requirement with the LEAST operational effort?

AUse AWS Database Migration Service (AWS DMS) Schema Conversion to migrate the schema. Use AWS DMS to migrate the data.

BUse the AWS Schema Conversion Tool (AWS SCT) to migrate the schema. Use AWS Database Migration Service (AWS DMS) to migrate the data.

CUse AWS Database Migration Service (AWS DMS) to migrate the data. Use automatic schema conversion.

DManually export the schema definition from Teradata. Apply the schema to the Amazon Redshift database. Use AWS Database Migration Service (AWS DMS) to migrate the data.

Answer: B

Question: 2

A company stores sensitive transaction data in an Amazon S3 bucket. A data engineer must implement controls to prevent accidental deletions.

AEnable versioning on the S3 bucket and configure MFA delete.

BConfigure an S3 bucket policy rule that denies the creation of S3 delete markers.

CCreate an S3 Lifecycle rule that moves deleted files to S3 Glacier Deep Archive.

DSet up AWS Config remediation actions to prevent users from deleting S3 objects.

Answer: A

Explanation:

''To protect data from accidental deletion, enable S3 Versioning and MFA Delete, which requires MFA for object deletions and prevents unintentional loss.''

-- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This is the AWS best practice for securing critical S3 data.

Question: 3

A data engineer develops an AWS Glue Apache Spark ETL job to perform transformations on a dataset. When the data engineer runs the job, the job returns an error that reads, "No space left on device."

The data engineer needs to identify the source of the error and provide a solution.

Which combinations of steps will meet this requirement MOST cost-effectively? (Select TWO.)

AScale out the workers vertically to address data skewness.

BUse the Spark UI and AWS Glue metrics to monitor data skew in the Spark executors.

CScale out the number of workers horizontally to address data skewness.

DEnable the --write-shuffle-files-to-s3 job parameter. Use the salting technique.

EUse error logs in Amazon CloudWatch to monitor data skew.

Answer: B, D

Explanation:

A ''No space left on device'' error typically results from data skew or large shuffle stages. The best actions are:

B . Monitor using Spark UI and Glue metrics to find skewed partitions or executor issues.

D . Use --write-shuffle-files-to-s3 to offload intermediate data to S3 instead of local disk, and apply salting to reduce skew.

''You can reduce the impact of data skew and large shuffle operations by monitoring with Spark UI and enabling the --write-shuffle-files-to-s3 option. Salting can help rebalance the skewed keys.''

-- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Scaling out workers (A, C) is more costly and less efficient if the root cause (skew) is not fixed.

Question: 4

A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.

A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.

How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?

AUse a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.

BUse the Amazon Redshift Data API to publish an event to Amazon EventBridqe. Configure an EventBridge rule to invoke the Lambda function.

CUse the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.

DUse a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.

Answer: C

Explanation:

The Amazon Redshift Data API enables you to interact with your Amazon Redshift data warehouse in an easy and secure way. You can use the Data API to run SQL commands, such as loading data into tables, without requiring a persistent connection to the cluster. The Data API also integrates with Amazon EventBridge, which allows you to monitor the execution status of your SQL commands and trigger actions based on events. By using the Data API to publish an event to EventBridge, the data engineer can invoke the Lambda function that writes the load statuses to the DynamoDB table. This solution is scalable, reliable, and cost-effective. The other options are either not possible or not optimal. You cannot use a second Lambda function to invoke the first Lambda function based on CloudWatch or CloudTrail events, as these services do not capture the load status of Redshift tables. You can use the Data API to publish a message to an SQS queue, but this would require additional configuration and polling logic to invoke the Lambda function from the queue. This would also introduce additional latency and cost. Reference:

Using the Amazon Redshift Data API

Using Amazon EventBridge with Amazon Redshift

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift

Question: 5

A company uses Amazon Redshift as a data warehouse solution. One of the datasets that the company stores in Amazon Redshift contains data for a vendor.

Recently, the vendor asked the company to transfer the vendor's data into the vendor's Amazon S3 bucket once each week.

Which solution will meet this requirement?

ACreate an AWS Lambda function to connect to the Redshift data warehouse. Configure the Lambda function to use the Redshift COPY command to copy the required data to the vendor's S3 bucket on a schedule.

BCreate an AWS Glue job to connect to the Redshift data warehouse. Configure the AWS Glue job to use the Redshift UNLOAD command to load the required data to the vendor's S3 bucket on a schedule.

CUse the Amazon Redshift data sharing feature. Set the vendor's S3 bucket as the destination. Configure the source to be as a custom SQL query that selects the required data.

DConfigure Amazon Redshift Spectrum to use the vendor's S3 bucket as destination. Enable data querying in both directions.

Answer: B

Explanation:

The Redshift UNLOAD command is specifically designed to export query results to Amazon S3, and AWS Glue can orchestrate this as part of a scheduled job. This is the cleanest and most appropriate approach for recurring weekly data transfers:

''Use the Redshift UNLOAD command with AWS Glue to export data to Amazon S3. This pattern enables routine exports of selected data to external locations.''

-- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This avoids complexities of Redshift Spectrum or unsupported use of COPY commands in Lambda.

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