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 sales company uses AWS Glue ETL to collect, process, and ingest data into an Amazon S3 bucket. The AWS Glue pipeline creates a new file in the S3 bucket every hour. File sizes vary from 200 KB to 300 KB. The company wants to build a sales prediction model by using data from the previous 5 years. The historic data includes 44,000 files. The company builds a second AWS Glue ETL pipeline by using the smallest worker type. The second pipeline retrieves the historic files from the S3 bucket and processes the files for downstream analysis. The company notices significant performance issues with the second ETL pipeline. The company needs to improve the performance of the second pipeline. Which solution will meet this requirement MOST cost-effectively?

A : Use a larger worker type.

B : Increase the number of workers in the AWS Glue ETL jobs.

C : Use the AWS Glue DynamicFrame grouping option.

D : Enable AWS Glue auto scaling.

Answer: D

Question 3

An analytics team frequently runs complex Amazon Athena queries on a dataset stored in an Amazon S3 bucket, with AWS Glue Data Catalog serving as the metadata repository. They've identified a slowdown in Athena query plans and attribute it to the excessive number of S3 partitions. The team needs to enhance query performance and reduce the time Athena takes in query planning.

Which two measures should the team take to meet these performance goals? (Select TWO.)

A : Implement AWS Glue Elastic Views to automate partition management.

B : Employ Athena's partition projection for dynamic partitioning based on common query patterns.

C : Merge small S3 objects into larger ones using AWS Glue's data transformation jobs.

D : Use Athena DML statements to reorganize data into fewer, more meaningful partitions.

E : Transform the data that is in the S3 bucket to Apache Parquet format.

Answer: B,E

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 Data Engineering Team is tasked with setting up a cloud-based workflow to orchestrate a series of ETL jobs and analytics tasks. They need a solution that requires minimal setup complexity. The workflow includes extracting data from various sources, transforming it using AWS Glue, and loading the results into an Amazon Redshift cluster for querying. The pipeline must manage task dependencies, retries, and error handling efficiently.

Given the need for minimal setup complexity, which AWS service would be most suitable for orchestrating this data pipeline?

A : Configure a series of Amazon EventBridge (formerly CloudWatch Events) rules to manage the workflow. Each rule would trigger the next process in the ETL pipeline upon successful completion of the previous one.

B : Use AWS Lambda to sequentially trigger each task, where each Lambda function starts the next stage in the pipeline upon the successful completion of the previous one.

C : Implement Amazon Managed Workflows for Apache Airflow (MWAA) to build a robust pipeline. MWAA offers task dependency management, scheduling, and monitoring.

D : Utilize AWS Step Functions to orchestrate the tasks. Step Functions provide a user-friendly interface for visual workflow management, automated retries, error handling, and integration with AWS Glue and Redshift, all with relatively less setup complexity.

Answer: D