100% Free Amazon AWS-DEA-C01 Practice Test Questions

Question 1

A company needs to load customer data that comes from a third party into an Amazon Redshift datawarehouse. The company stores order data and product data in the same data warehouse. The company wantsto use the combined dataset to identify potential new customers.A data engineer notices that one of the fields in the source data includes values that are in JSON format.How should the data engineer load the JSON data into the data warehouse with the LEAST effort?

A : Use the SUPER data type to store the data in the Amazon Redshift table.

B : Use AWS Glue to flatten the JSON data and ingest it into the Amazon Redshift table.

C : Use Amazon S3 to store the JSON data. Use Amazon Athena to query the data.

D : Use an AWS Lambda function to flatten the JSON data. Store the data in Amazon S3.

Answer: A

Question 2

A Cloud Data Engineer is designing a serverless data processing pipeline on AWS. The pipeline uses AWS Lambda for data transformation, AWS Step Functions for workflow management, and Amazon DynamoDB for storing intermediate data. The engineer decides to use the AWS Serverless Application Model (AWS SAM) to manage the deployment of these components. The primary requirement is to ensure that the deployment process is repeatable and easily modifiable.

What should the engineer include in the SAM template to meet these requirements efficiently?

A : Include the Lambda functions and DynamoDB tables in the SAM template, but exclude the Step Functions, as they are not supported by AWS SAM.

B : Specify only the Lambda function in the SAM template, manually configuring the Step Functions and DynamoDB tables through the AWS Console.

C : Use separate SAM templates for each component (Lambda, Step Functions, DynamoDB) to manage deployments individually for better control.

D : Define all the components (Lambda functions, Step Functions, DynamoDB tables) in the SAM template, and use AWS CloudFormation to deploy the entire pipeline

Answer: D

Question 3

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

A : 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.

B : 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.

C : 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.

D : 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: B

Question 4

A company wants to migrate an application and an on-premises Apache Kafka server to AWS. Theapplication processes incremental updates that an on-premises Oracle database sends to the Kafka server. Thecompany wants to use the replatform migration strategy instead of the refactor strategy.Which solution will meet these requirements with the LEAST management overhead?

A : Amazon Kinesis Data Streams

B : Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned cluster

C : Amazon Data Firehose

D : Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless

Answer: D

Question 5

A company has three subsidiaries. Each subsidiary uses a different data warehousing solution. The firstsubsidiary hosts its data warehouse in Amazon Redshift. The second subsidiary uses Teradata Vantage onAWS. The third subsidiary uses Google BigQuery.The company wants to aggregate all the data into a central Amazon S3 data lake. The company wants to useApache Iceberg as the table format.A data engineer needs to build a new pipeline to connect to all the data sources, run transformations by usingeach source engine, join the data, and write the data to Iceberg.Which solution will meet these requirements with the LEAST operational effort?

A : Use native Amazon Redshift, Teradata, and BigQuery connectors to build the pipeline in AWS Glue. Use native AWS Glue transforms to join the data. Run a Merge operation on the data lake Iceberg table.

B : Use the Amazon Athena federated query connectors for Amazon Redshift, Teradata, and BigQuery to build the pipeline in Athena. Write a SQL query to read from all the data sources, join the data, and run a Merge operation on the data lake Iceberg table

C : Use the native Amazon Redshift connector, the Java Database Connectivity (JDBC) connector for Teradata, and the open source Apache Spark BigQuery connector to build the pipeline in Amazon EMR. Write code in PySpark to join the data. Run a Merge operation on the data lake Iceberg table.

D : Use the native Amazon Redshift, Teradata, and BigQuery connectors in Amazon Appflow to write data to Amazon S3 and AWS Glue Data Catalog. Use Amazon Athena to join the data. Run a Merge operation on the data lake Iceberg table.

Answer: B

Free Practice Amazon AWS-DEA-C01 Exam Questions 2025