Question 1

While reviewing the data security policies of their company, a Cloud Data Engineering Consultant identifies a need to enhance the protection of sensitive data stored in various AWS services such as Amazon S3, RDS, and DynamoDB.

Which method would be most effective in securing the data across these services to prevent unauthorized access?

Options :

A : Encrypt the data at rest using AWS KMS-managed keys and ensure that encryption in transit is enabled for all communications.

B : Rely solely on AWS IAM policies to govern access, granting necessary permissions to each service based on the role of the user or service accessing the data.

C : Implement network access control lists (NACLs) and security groups to restrict access to the resources based on IP addresses.

D : Place all data in a private VPC and disable internet access, requiring all access to occur through a VPN or Direct Connect.

Answer: A

Question 2

A Cloud Data Engineer is designing a serverless data processing pipeline on AWS. The pipeline uses AWS Lambda for data transformation, AWS Step Functions for workflow management, and Amazon DynamoDB for storing intermediate data. The engineer decides to use the AWS Serverless Application Model (AWS SAM) to manage the deployment of these components. The primary requirement is to ensure that the deployment process is repeatable and easily modifiable.

What should the engineer include in the SAM template to meet these requirements efficiently?

Options :

A : Include the Lambda functions and DynamoDB tables in the SAM template, but exclude the Step Functions, as they are not supported by AWS SAM.

B : Specify only the Lambda function in the SAM template, manually configuring the Step Functions and DynamoDB tables through the AWS Console.

C : Use separate SAM templates for each component (Lambda, Step Functions, DynamoDB) to manage deployments individually for better control.

D : Define all the components (Lambda functions, Step Functions, DynamoDB tables) in the SAM template, and use AWS CloudFormation to deploy the entire pipeline

Answer: D

Question 3

A company uses an Amazon S3 bucket to store data in both JSON and .csv formats. They also operate databases on Amazon RDS for Microsoft SQL Server, have Amazon DynamoDB tables in provisioned capacity mode, and run queries on an Amazon Redshift cluster. They need a straightforward solution that allows their data scientists to perform SQL-like queries across all these data sources.

What is the simplest solution for enabling these queries with the least operational overhead?

Options :

A : Enable AWS Glue to catalog the data sources and use Amazon Athena with standard SQL for structured data and PartiQL for the JSON data.

B : Use AWS Glue to catalog and transform JSON data into .csv format using Glue jobs, then query all data using Amazon Athena.

C : Catalog the data with AWS Glue, and query using Redshift Spectrum, applying standard SQL to structured data and PartiQL to JSON.

D : Set up a data lake with AWS Lake Formation, convert all data to Apache Parquet using Lake Formation jobs, and query the data using Amazon Athena or Redshift Spectrum.

Answer: A

Question 4

A company wants to migrate an application and an on-premises Apache Kafka server to AWS. The

application processes incremental updates that an on-premises Oracle database sends to the Kafka server. The

company wants to use the replatform migration strategy instead of the refactor strategy.

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

Options :

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 company has completed a full database load to their S3 data lake and is now using AWS Database Migration Service (DMS) to stream ongoing change data capture (CDC) updates. They need an effective method to apply these CDC updates, including deletes and upserts, to their structured data in S3.

What is the most efficient and transactionally consistent approach for applying these updates?

Options :

A : Trigger AWS Lambda functions for each CDC update to apply changes to the S3 data.

B : Implement Amazon Redshift Spectrum to manage and apply CDC updates to the structured data in the S3 data lake

C : Use AWS Glue with custom ETL scripts to process and apply the CDC changes to the data in S3

D : Structure the data in Apache Iceberg format on S3 and use AWS Glue to apply CDC logic

Answer: D