Question 1

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.)

Options :

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 2

A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The

company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform

analytics based on the data.

Which AWS service or feature will meet these requirements with the LEAST operational overhead?

Options :

A : Amazon Managed Streaming for Apache Kafka (Amazon MSK)

B : Amazon AppFlow

C : AWS Glue Data Catalog

D : Amazon Kinesis

Answer: B

Question 3

A company stores CSV files in an Amazon S3 bucket. A data engineer needs to process the data in the CSV

files and store the processed data in a new S3 bucket.

The process needs to rename a column, remove specific columns, ignore the second row of each file, create a

new column based on the values of the first row of the data, and filter the results by a numeric value of a

column.

Which solution will meet these requirements with the LEAST development effort?

Options :

A : Use AWS Glue Python jobs to read and transform the CSV files.

B : Use an AWS Glue custom crawler to read and transform the CSV files.

C : Use an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files.

D : Use AWS Glue DataBrew recipes to read and transform the CSV files.

Answer: D

Question 4

A data engineer is managing several AWS Lambda functions that rely on a common set of Python libraries for data transformation tasks. Currently, when updates are made to these libraries, the data engineer must manually update the code for each Lambda function. The data engineer seeks a more efficient method to manage and distribute these shared libraries across multiple Lambda functions.

Which approach should the data engineer take to streamline the process of updating the shared Python libraries in all the Lambda functions?

Options :

A : Utilize AWS CodeCommit to store the Python scripts and set up a CI/CD pipeline to automatically deploy updates to the Lambda functions.

B : Package the shared Python scripts into a Lambda Layer and associate this layer with all the relevant Lambda functions.

C : Implement an AWS Step Functions workflow to orchestrate the updates of the Python scripts across all Lambda functions simultaneously.

D : Create an Amazon Elastic File System (EFS) volume with the Python scripts and mount it across all the Lambda functions.

Answer: B

Question 5

A data analytics team is working with time-sensitive financial data. Their primary dataset resides in an Amazon Redshift cluster, but crucial, frequently updated transactional data is stored in a PostgreSQL database on Amazon RDS. They need a solution to analyze combined data sets in real-time, ensuring that the most current transactional information is included in their analysis without the overhead of continually transferring data between the two systems.

What approach should they consider to efficiently perform these real-time, integrated queries?

Options :

A : Utilize Athena Federated Queries to perform real-time querying across both the Redshift data and the RDS PostgreSQL database.

B : Implement Redshift Spectrum for querying external data, creating external tables that reflect the RDS PostgreSQL data.

C : Regularly export transactional data from RDS PostgreSQL to S3 and use Redshift Spectrum for querying alongside Redshift data.

D : Use Redshift Federated Query to directly integrate and query data from the Redshift cluster and the RDS PostgreSQL database in real-time.

Answer: D

Confidently Practice Online with Free Data-Engineer-Associate Exam Cram

Buying Options: