Question 1

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 2

A company stores its processed data in an S3 bucket. The company has a strict data access policy. The

company uses IAM roles to grant teams within the company different levels of access to the S3 bucket.

The company wants to receive notifications when a user violates the data access policy. Each notification

must include the username of the user who violated the policy.

Which solution will meet these requirements?

Options :

A : Use AWS Config rules to detect violations of the data access policy. Set up compliance alarms.

B : Use Amazon CloudWatch metrics to gather object-level metrics. Set up CloudWatch alarms.

C :

Use AWS CloudTrail to track object-level events for the S3 bucket. Forward events to Amazon CloudWatch to set up CloudWatch alarms.

D :

Use Amazon S3 server access logs to monitor access to the bucket. Forward the access logs to an Amazon CloudWatch log group. Use metric filters on the log group to set up CloudWatch alarms.

Answer: C

Question 3

A Data Engineering team is working on deploying a serverless data processing solution that includes AWS Lambda functions, AWS Step Functions for orchestrating workflows, and Amazon DynamoDB tables for storing processed data. The team needs a streamlined, manageable, and repeatable way to deploy and update this serverless infrastructure across multiple AWS accounts and regions. They also require the ability to roll back changes in case of errors and to manage different configurations for testing and production environments.

Which of the following deployment strategies would best fit these requirements?

Options :

A : Rely on Terraform as an infrastructure as code solution to define and deploy all components. Use Terraform workspaces to manage different configurations for testing and production environments.

B : Develop a unified AWS SAM template incorporating the Lambda functions, Step Functions, and DynamoDB tables. Utilize the AWS SAM CLI to deploy to different accounts and regions, managing different configurations with environment-specific SAM parameter files.

C : Use AWS CloudFormation with separate templates for each service (Lambda, Step Functions, DynamoDB). Handle different configurations for testing and production using parameter files. Manage deployments and updates manually through the AWS Management Console.

D : Implement an AWS Elastic Beanstalk application to manage the deployment and versioning of the serverless components. Configure separate environments within Elastic Beanstalk for testing and production.

Answer: B

Question 4

A pharmaceutical company is conducting extensive research and development. Their scientists are running numerous complex computational simulations and bioinformatics algorithms. These tasks are batch processing jobs that can run independently but are computationally intensive.

The company needs to manage these jobs efficiently on AWS, ensuring optimal resource utilization and cost-effectiveness. They also require the ability to schedule jobs, manage dependencies, and handle job retries automatically.

What is the most suitable AWS solution to meet these requirements?

Options :

A : Implement the batch processing using Amazon EMR, with Amazon DynamoDB to manage job queues, and AWS Lambda coupled with Amazon CloudWatch for scheduling and error handling.

B : Set up the computational tasks on Amazon EC2 instances, managed by AWS Elastic Beanstalk for scaling and deployment, using Amazon EventBridge for job scheduling and dependencies.

C : Deploy the computational tasks in Amazon EC2 Auto Scaling groups, utilizing Amazon CloudWatch Events for scheduling and AWS Lambda for dependency management and job retries.

D : Use AWS Batch to manage and run batch jobs, with Amazon S3 for input data storage. Leverage AWS Step Functions to manage job workflows and dependencies.

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

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