Question 1

A company stores daily records of the financial performance of investment portfolios in .csv format in an

Amazon S3 bucket. A data engineer uses AWS Glue crawlers to crawl the S3 data.

The data engineer must make the S3 data accessible daily in the AWS Glue Data Catalog.

Which solution will meet these requirements?

Options :

A :

Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler-s data store. Create a daily schedule to run the crawler. Configure the output destination to a new path in the existing S3 bucket.

B :

Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler-s data store. Create a daily schedule to run the crawler. Specify a database name for the output.

C :

Create an IAM role that includes the AmazonS3FullAccess policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler-s data store. Allocate data processing units (DPUs) to run the crawler every day. Specify a database name for the output.

D :

Create an IAM role that includes the AWSGlueServiceRole policy. Associate the role with the crawler. Specify the S3 bucket path of the source data as the crawler-s data store. Allocate data processing units (DPUs) to run the crawler every day. Configure the output destination to a new path in the existing S3 bucket.

Answer: B

Question 2

A data engineering team needs to manage and query a continuously evolving dataset of customer transactions stored in Amazon S3. They use Amazon Athena for querying and require an automated solution to handle schema changes in the dataset.

What AWS service should the team use to efficiently manage the schema and ensure seamless integration with Athena for updated data queries?

Options :

A : Implement AWS Data Pipeline for automated data transfer and schema updates.

B : Use AWS Glue Data Catalog for schema management and metadata storage.

C : Apply Amazon RDS for automated database management and schema updates.

D : Configure Amazon Redshift Spectrum to manage schema changes.

Answer: B

Question 3

A company receives test results from testing facilities that are located around the world. The company stores

the test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process the

files, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The data

engineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and Amazon

EventBridge to schedule jobs.

The company recently added more testing facilities. The time required to process files is increasing. The data

engineer must reduce the data processing time.

Which solution will MOST reduce the data processing time?

Options :

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 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.)

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 5

A company uses Amazon Athena for one-time queries against data that is in Amazon S3. The company has

several use cases. The company must implement permission controls to separate query processes and access to

query history among users, teams, and applications that are in the same AWS account.

Which solution will meet these requirements?

Options :

A :

Create an S3 bucket for each use case. Create an S3 bucket policy that grants permissions to appropriate individual IAM users. Apply the S3 bucket policy to the S3 bucket.

B :

Create an Athena workgroup for each use case. Apply tags to the workgroup. Create an 1AM policy that uses the tags to apply appropriate permissions to the workgroup.

C :

Create an JAM role for each use case. Assign appropriate permissions to the role for each use case. Associate the role with Athena.

D :

Create an AWS Glue Data Catalog resource policy that grants permissions to appropriate individual IAM users for each use case. Apply the resource policy to the specific tables that Athena uses.

Answer: B

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