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Total 485 Questions | Updated On: Jan 08, 2025

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Question 1

A data engineer needs to use AWS Step Functions to design an orchestration workflow. The workflow must parallel process a large collection of data files and apply a specific transformation to each file. Which Step Functions state should the data engineer use to meet these requirements?

A : Parallel state

B : Choice state

C : Map state

D : Wait state

Answer: C

Question 2

A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets andAmazon RDS databases. The data catalog must include storage format metadata for the data in the catalog.Which solution will meet these requirements with the LEAST effort?

A : Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog. Use data stewards to inspect the data and update the data catalog with the data format.

B : Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.

C : Use Amazon Macie to build a data catalog and to identify sensitive data elements. Collect the data format information from Macie.

D : Use scripts to scan data elements and to assign data classifications based on the format of the data.

Answer: B

Question 3

A company created an extract, transform, and load (ETL) data pipeline in AWS Glue. A data engineer must

crawl a table that is in Microsoft SQL Server. The data engineer needs to extract, transform, and load the

output of the crawl to an Amazon S3 bucket. The data engineer also must orchestrate the data pipeline.

Which AWS service or feature will meet these requirements MOST cost-effectively?

A : AWS Step Functions

B : AWS Glue workflows

C : AWS Glue Studio

D : Amazon Managed Workflows for Apache Airflow (Amazon MWAA)

Answer: B

Question 4

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 5

A data engineer uses Amazon Redshift to run resource-intensive analytics processes once every month. Every

month, the data engineer creates a new Redshift provisioned cluster. The data engineer deletes the Redshift

provisioned cluster after the analytics processes are complete every month. Before the data engineer deletes

the cluster each month, the data engineer unloads backup data from the cluster to an Amazon S3 bucket.

The data engineer needs a solution to run the monthly analytics processes that does not require the data

engineer to manage the infrastructure manually.

Which solution will meet these requirements with the LEAST operational overhead?

A : Use Amazon Step Functions to pause the Redshift cluster when the analytics processes are complete and to resume the cluster to run new processes every month.

B : Use Amazon Redshift Serverless to automatically process the analytics workload.

C : Use the AWS CLI to automatically process the analytics workload.

D : Use AWS CloudFormation templates to automatically process the analytics workload.

Answer: B

Page: 1 / 97

Total 485 Questions | Updated On: Jan 08, 2025

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