Data-Engineer-Associate Exam Dumps - AWS Certified Data Engineer - Associate (DEA-C01)

To meet both encryption at rest and in transit, a single Amazon EMR security configuration can be created specifying the AWS KMS key for encryption at rest and the PEM file for in-transit encryption. The study guide clearly states:

“AWS Key Management Service (KMS) provides encryption for data at rest, and SSL/TLS ensures encryption for data in transit, providing end-to-end encryption within an AWS environment.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

A single security configuration is sufficient and the cleanest way to apply these security features during EMR cluster setup.

AWS Step Functions is a service that allows you to coordinate multiple AWS services into serverless workflows. You can use Step Functions to create state machines that define the sequence and logic of the tasks in your workflow. Step Functions supports various types of tasks, such as Lambda functions, AWS Glue jobs, Amazon EMR clusters, Amazon ECS tasks, etc. You can use Step Functions to monitor and troubleshoot your workflows, as well as to handle errors and retries.

Using an AWS Step Functions workflow that includes a state machine to run the Lambda function and then the AWS Glue job will meet the requirements with the least management overhead, as it leverages the serverless and managed capabilities of Step Functions. You do not need to write any code to orchestrate the tasks in your workflow, as you can use the Step Functions console or the AWS Serverless Application Model (AWS SAM) to define and deploy your state machine. You also do not need to provision or manage any servers or clusters, as Step Functions scales automatically based on the demand.

The other options are not as efficient as using an AWS Step Functions workflow. Using an Apache Airflow workflow that is deployed on an Amazon EC2 instance or on Amazon Elastic Kubernetes Service (Amazon EKS) will require more management overhead, as you will need to provision, configure, and maintain the EC2 instance or the EKS cluster, as well as the Airflow components. You will also need to write and maintain the Airflow DAGs to orchestrate the tasks in your workflow. Using an AWS Glue workflow to run the Lambda function and then the AWS Glue job will not work, as AWS Glue workflows only support AWS Glue jobs and crawlers as tasks, not Lambda functions. References:

AWS Step Functions

AWS Glue

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 6: Data Integration and Transformation, Section 6.3: AWS Step Functions

Problem Analysis:

The pipeline processes compressed files in S3 and must support incremental data processing.

AWS Glue features must facilitate tracking progress to avoid reprocessing the same data.

Key Considerations:

Incremental data processing requires tracking which files or partitions have already been processed.

The solution must be automated and efficient for large-scale ETL jobs.

Solution Analysis:

Option A: Workflows

Workflows organize and orchestrate multiple Glue jobs but do not track progress for incremental data processing.

Option B: Triggers

Triggers initiate Glue jobs based on a schedule or events but do not track which data has been processed.

Option C: Job Bookmarks

Job bookmarks track the state of the data that has been processed, enabling incremental processing.

Automatically skip files or partitions that were previously processed in Glue jobs.

Option D: Classifiers

Classifiers determine the schema of incoming data but do not handle incremental processing.

Final Recommendation:

Job bookmarks are specifically designed to enable incremental data processing in AWS Glue ETL pipelines.

AWS Glue Job Bookmarks Documentation

AWS Glue ETL Features

AWS Lake Formation is a service that allows you to easily set up, secure, and manage data lakes. One of the features of Lake Formation is row-level security, which enables you to control access to specific rows or columns of data based on the identity or role of the user. This feature is useful for scenarios where you need to restrict access to sensitive or regulated data, such as customer data from different countries. By registering the S3 bucket as a data lake location in Lake Formation, you can use the Lake Formation console or APIs to define and apply row-level security policies to the data in the bucket. You can also use Lake Formation blueprints to automate the ingestion and transformation of data from various sources into the data lake. This solution requires the least operational effort compared to the other options, as it does not involve creating or moving data, or managing multiple tables, views, or roles. References:

AWS Lake Formation

Row-Level Security

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Lakes and Data Warehouses, Section 4.2: AWS Lake Formation