Free Amazon AWS-DEA-C01 Exam Questions

Question 1

A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary. The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job. Which rule will meet these requirements?

A : DatasetMatch "reference" "city->ref_city, state->ref_state" = 1.0

B : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 1.0

C : DatasetMatch "reference" "city->ref_city, state->ref_state" = 100

D : ReferentialIntegrity "city,state" "reference.{ref_city,ref_state}" = 100

Answer: A

Question 2

A photography studio stores its high-resolution image files in an Amazon S3 bucket. Recently, they encountered issues with accidental overwrites of important files. The studio requires a solution to prevent the overwriting and accidental deletion of their images while ensuring that the latest version of a file is readily accessible.

Which single feature should the photography studio enable on their S3 bucket to address this issue effectively?

A : Activate S3 Versioning to maintain multiple versions of each file.

B : Enable S3 Object Lock in compliance mode for strict WORM (Write Once, Read Many) protection.

C : Configure S3 Intelligent-Tiering to manage the storage of the files efficiently.

D : Implement S3 Lifecycle policies to archive older versions of files to S3 Glacier.

Answer: A

Question 3

A mobile app tracks user activity data, which is continuously streamed to Amazon Kinesis Data Streams. The app requires a solution to process this data in real-time and update user profiles stored in Amazon DynamoDB based on the activity data.

What combination of AWS services should be used for real-time processing of the stream and updating the user profiles in DynamoDB?

A : Use AWS Lambda to process data from Kinesis Data Streams and update records in Amazon DynamoDB.

B : Configure Amazon EMR to handle stream processing and update DynamoDB with batch jobs.

C : Deploy Amazon EC2 instances to consume and process the stream, then write to DynamoDB.

D : Use AWS Glue to extract and transform streaming data, and load into DynamoDB.

Answer: A

Question 4

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

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

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

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

D : Manually 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 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