Free Amazon AWS-DEA-C01 Exam Questions

Question 1

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 2

A Data Engineering Team is responsible for managing and monitoring a large-scale data processing application using AWS services. The application periodically processes vast amounts of unstructured data and stores the results in Amazon S3. The team needs to set up a system to closely monitor the data processing jobs and get alerted if any job fails or underperforms.

Which combination of AWS services should the data engineering team use for effective monitoring and alerting?

A : Leverage AWS Step Functions to manage the data processing workflows and use Amazon CloudWatch alarms to monitor each step's execution status.

B : Use Amazon CloudWatch for monitoring, combined with AWS Lambda functions to check the health of the data processing jobs and send notifications using Amazon SNS.

C : Implement Amazon QuickSight for real-time analytics on the job performance data and set up Amazon SES to email the reports to the team.

D : Utilize AWS X-Ray for tracing the processing jobs and integrate it with Amazon SES to alert the team about any anomalies or failures.

Answer: B

Question 3

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 4

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

Question 5

A Data Engineering Team is setting up a new job in AWS Glue ETL. They have configured their data sources and targets, defined the necessary transformations, but when they attempt to run the job, it fails with an authorization error. The team needs to ensure that the AWS Glue job has the necessary permissions to access the required AWS resources for execution.

What should the team do to resolve this issue and successfully run the Glue ETL job?

A : Enable AWS Glue Data Catalog encryption, which automatically grants necessary permissions to the ETL job.

B : Attach an IAM policy directly to the AWS Glue ETL job with the necessary permissions.

C : Create an IAM group with the appropriate permissions and add the AWS Glue service account to this group.

D : Assign an AWS Glue Service Role to the ETL job, ensuring it has the required permissions to access necessary resources.

Answer: D