Question 1

A data engineer must ingest a source of structured data that is in .csv format into an Amazon S3 data lake. The

.csv files contain 15 columns. Data analysts need to run Amazon Athena queries on one or two columns of the

dataset. The data analysts rarely query the entire file.

Which solution will meet these requirements MOST cost-effectively?

Options :

A : Use an AWS Glue PySpark job to ingest the source data into the data lake in .csv format.

B : Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source.

Configure the job to ingest the data into the data lake in JSON format.

C : Use an AWS Glue PySpark job to ingest the source data into the data lake in Apache Avro format.

D :

Create an AWS Glue extract, transform, and load (ETL) job to read from the .csv structured data source. Configure the job to write the data into the data lake in Apache Parquet format.

Answer: D

Question 2

A data engineer is configuring an AWS Glue job to read data from an Amazon S3 bucket. The data engineer

has set up the necessary AWS Glue connection details and an associated IAM role. However, when the data

engineer attempts to run the AWS Glue job, the data engineer receives an error message that indicates that

there are problems with the Amazon S3 VPC gateway endpoint.

The data engineer must resolve the error and connect the AWS Glue job to the S3 bucket.

Which solution will meet this requirement?

Options :

A :

Update the AWS Glue security group to allow inbound traffic from the Amazon S3 VPC gateway endpoint.

B :

Configure an S3 bucket policy to explicitly grant the AWS Glue job permissions to access the S3 bucket.

C :

Review the AWS Glue job code to ensure that the AWS Glue connection details include a fully qualified domain name.

D :

Verify that the VPC-s route table includes inbound and outbound routes for the Amazon S3 VPC gateway endpoint.

Answer: D

Question 3

A data engineer wants to orchestrate a set of extract, transform, and load (ETL) jobs that run on AWS. The

ETL jobs contain tasks that must run Apache Spark jobs on Amazon EMR, make API calls to Salesforce, and

load data into Amazon Redshift.

The ETL jobs need to handle failures and retries automatically. The data engineer needs to use Python to

orchestrate the jobs.

Which service will meet these requirements?

Options :

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

B : AWS Step Functions

C : AWS Glue

D : Amazon EventBridge

Answer: A

Question 4

A company plans to migrate its on-premises Oracle database to Amazon Aurora PostgreSQL. They need to ensure a seamless migration of both the database schema and the data with minimal downtime. The company also requires a tool to convert the source database schema to be compatible with Amazon Aurora PostgreSQL.

Which combination of AWS services should the company use to migrate the database schema and data?

Options :

A : Utilize Amazon RDS for data migration and AWS Lambda for schema conversion.

B : Use AWS Database Migration Service (DMS) for data migration and AWS Schema Conversion Tool (SCT) for schema conversion.

C : Implement AWS Glue for both schema conversion and data migration.

D : Apply Amazon S3 transfer acceleration for data migration and Amazon Athena for schema conversion.

Answer: B

Question 5

A Cloud Data Engineering Team is implementing a system for real-time data ingestion through an API. The architecture needs to include data transformation before storage. The system must handle large files and store them efficiently post-transformation. The team is focused on using a serverless architecture on AWS, with an emphasis on Infrastructure as Code (IaC) for standardized and repeatable deployments across various environments.

Which combination of actions should the Cloud Data Engineering Team take to implement IaC for serverless deployments of data ingestion and transformation pipelines? (Select THREE)

Options :

A : Set up AWS Data Pipeline in the AWS SAM template for data movement and transformation.

B : Use AWS Elastic MapReduce (EMR) for data transformation in Kinesis Firehose.

C : Define an Amazon API Gateway in the AWS SAM template for data ingestion.

D : Configure DynamoDB in the AWS SAM template for storing the transformed data.

E : Utilize AWS Serverless Application Model (SAM) to declare AWS Lambda functions integrated with Amazon Kinesis Data Firehose.

F : Configure Amazon S3 bucket creation in the AWS SAM template for storing the transformed data.

Answer: C,E,F

Free AWS-DEA-C01 Mock Exam – Practice Online Confidently

Buying Options: