Question 1

A multinational corporation operates in multiple AWS Regions and each region has a dedicated research team. The company maintains a collection of proprietary datasets in an Amazon S3-based data warehouse.

A data engineering team has been tasked with ensuring that each research team can only query datasets pertaining to their specific region. The solution should be scalable and maintain minimal operational overhead.

Which combination of actions should the data engineering team take to enforce this requirement with the least amount of operational overhead? (Select TWO.)

Options :

A : Create individual S3 endpoints for each region through Amazon VPC and associate them with respective IAM roles for the research teams.

B : Apply tag-based access control on S3 objects, tagging each dataset with its corresponding region and modifying IAM roles to enforce these tags.

C : Implement S3 bucket policies that conditionally grant access based on the originating region of the request.

D : Use AWS Lake Formation to set up cross-account data sharing and enforce region-based access controls to the datasets.

E : Register each regional dataset with separate Amazon Redshift Spectrum external schemas and restrict access using IAM policies.

Answer: B,D

Question 2

A company is deploying a stateful application on Amazon EC2 that requires temporary storage for processing large datasets with maximum I/O. The application must also maintain a smaller subset of essential state data that needs to persist beyond the life of the instance. The company needs a storage solution that ensures the essential data is not lost if the EC2 instance is stopped or terminated.

Which combination of storage solutions should the company choose?

Options :

A : Attach Amazon EBS volumes for essential state data and use Amazon DynamoDB for temporary data.

B : Attach additional Amazon EBS volumes for temporary data processing and rely on the root device volume for essential state data.

C : Attach Amazon EC2 instance store volumes for temporary data processing and Amazon Elastic Block Store (EBS) for essential state data.

D : Implement a combination of Amazon EC2 instance store volumes for rapid I/O and EBS snapshots for daily backups of essential data.

Answer: C

Question 3

A Data Engineering team is optimizing their ETL pipeline and considering different strategies for intermediate data staging in Amazon S3. Their data processing involves multiple stages, each requiring different datasets and varying processing times. The team wants to structure the S3 bucket to efficiently manage and process these datasets.

Which bucket structure and naming strategy should the team implement for effective management and processing of the data at different stages of the ETL pipeline?

Options :

A : Use a single S3 bucket with a folder structure based on data sources rather than processing stages, and apply S3 Lifecycle policies to transition data to Amazon S3 Glacier as it moves through stages to optimize costs.

B : Organize the S3 bucket with top-level folders named after each stage of the ETL process (e.g., raw/, stage/, analytics/), and use timestamped subfolders within each to manage versions of the data

C : Structure the bucket with a flat hierarchy, naming files with a combination of dataset names and processing stages, followed by the processing timestamp (e.g., dataset-stage-timestamp.parquet), to facilitate simple retrieval and processing.

D : Adopt a layered S3 bucket strategy, creating separate buckets for Raw Data, Stage Data, and Analytics Data layers.

Answer: D

Question 4

A Data Engineering Team is tasked with enhancing the CI/CD workflow of their complex ETL pipeline on AWS. The pipeline involves frequent but varying updates—from minor feature additions to major architectural overhauls. The team uses GitHub for version control and manages a diverse array of AWS resources in their projects.

They require a CI/CD solution that not only automates build and test processes upon each commit but also allows for customizable, condition-based deployment strategies (e.g., deploying only specific branches or tags, or based on commit messages).

Given these requirements, which AWS service or combination of services would be the most effective?

Options :

A : AWS Lambda and AWS CodePipeline, where a Lambda function is triggered by GitHub webhooks to determine if the deployment should proceed, followed by CodePipeline managing the build, test, and deployment phases.

B : AWS CodeCommit and AWS CodePipeline, with CodeCommit to replace GitHub for enhanced integration within the AWS ecosystem, and CodePipeline to orchestrate the entire process including condition-based deployments.

C : AWS CodePipeline, integrating both GitHub and AWS CodeBuild. CodePipeline to manage the overall workflow and CodeBuild for the build and testing phases, leveraging pipeline actions for deployment conditions.

D : AWS CodeBuild and AWS CodeDeploy, with CodeBuild handling the build and test processes and CodeDeploy managing advanced deployment strategies based on GitHub webhook events.

Answer: C

Question 5

A company stores large image files in an Amazon S3 bucket. They need a solution to dynamically resize images on-the-fly when accessed by different applications, without storing multiple versions of the same image. The solution should modify the image data during retrieval based on the application's requirements.

Which AWS service should the company use to achieve this on-the-fly transformation of image data?

Options :

A : Amazon S3 Object Lambda

B : AWS Lambda@Edge

C : AWS Step Functions

D : Amazon Elastic Transcoder

Answer: A

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