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 company stores logs in an Amazon S3 bucket. When a data engineer attempts to access several log files, the

data engineer discovers that some files have been unintentionally deleted.

The data engineer needs a solution that will prevent unintentional file deletion in the future.

Which solution will meet this requirement with the LEAST operational overhead?

Options :

A : Manually back up the S3 bucket on a regular basis.

B : Enable S3 Versioning for the S3 bucket.

C : Configure replication for the S3 bucket.

D : Use an Amazon S3 Glacier storage class to archive the data that is in the S3 bucket.

Answer: B

Question 3

A Data Engineering Team is using AWS CloudFormation to automate the deployment of their data processing infrastructure, which includes an Amazon EMR cluster for big data processing. They need to ensure that the EMR cluster configuration can be dynamically updated based on different environments (development, staging, production) without creating separate templates for each environment.

Which specific CloudFormation feature should they use to meet this requirement most effectively?

Options :

A : Implement CloudFormation Custom Resources to enable custom processing logic and create environment-specific configurations for the EMR cluster.

B : Employ CloudFormation Mappings to define environment-specific values within the same template, allowing the template to dynamically alter the EMR cluster configuration based on the input environment type.

C : Use CloudFormation StackSets to manage multiple stacks across different accounts and regions with a single template, adapting to each environment-s specific requirements.

D : Utilize CloudFormation Parameter Labels to organize and manage the parameters for different environments, thereby allowing dynamic updates to the EMR cluster configuration based on the selected environment.

Answer: B

Question 4

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

Question 5

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?

Options :

A : Parallel state

B : Choice state

C : Map state

D : Wait state

Answer: C

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