Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

AWS Glue ETL is designed for scalable and serverless data processing, and it supports integrated quality enforcement using AWS Glue Data Quality, which makes it the most cost-effective and integrated option when combined with Amazon RDS for MySQL as the relational database.

“AWS Glue can perform data validation as part of the ETL process, ensuring data quality before storing the data in the target data store.”

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

Using AWS Glue Data Quality directly in the ETL workflow is simpler and more cost-effective than separating transformation (Glue) and validation (DataBrew) into different services.

 The Amazon Redshift Data API is a built-in feature that allows you to run SQL queries on Amazon Redshift data with web services-based applications, such as AWS Lambda, Amazon SageMaker notebooks, and AWS Cloud9. The Data API does not require a persistent connection to your database, and it provides a secure HTTP endpoint and integration with AWS SDKs. You can use the endpoint to run SQL statements without managing connections. The Data API also supports both Amazon Redshift provisioned clusters and Redshift Serverless workgroups. The Data API is the best solution for running real-time queries on the financial data from within the trading application, as it has the least operational overhead compared to the other options.

Option A is not the best solution, as establishing WebSocket connections to Amazon Redshift would require more configuration and maintenance than using the Data API. WebSocket connections are also not supported by Amazon Redshift clusters or serverless workgroups.

Option C is not the best solution, as setting up JDBC connections to Amazon Redshift would also require more configuration and maintenance than using the Data API. JDBC connections are also not supported by Redshift Serverless workgroups.

Option D is not the best solution, as storing frequently accessed data in Amazon S3 and using Amazon S3 Select to run the queries would introduce additional latency and complexity than using the Data API. Amazon S3 Select is also not optimized for real-time queries, as it scans the entire object before returning the results. References:

Using the Amazon Redshift Data API

Calling the Data API

Amazon Redshift Data API Reference

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

AWS Lake Formation is a service that helps you build, secure, and manage data lakes on Amazon S3. You can use AWS Lake Formation to register the S3 path as a data lake location, and enable fine-grained access control to limit access to the records based on the HR department’s Region. You can use data filters to specify which S3 prefixes or partitions each HR department can access, and grant permissions to the IAM roles of the HR departments accordingly. This solution will meet the requirement with the least operational overhead, as it simplifies the data lake management and security, and leverages the existing IAM roles of the HR departments12.

The other options are not optimal for the following reasons:

A. Use data filters for each Region to register the S3 paths as data locations. This option is not possible, as data filters are not used to register S3 paths as data locations, but to grant permissions to access specific S3 prefixes or partitions within a data location. Moreover, this option does not specify how to limit access to the records based on the HR department’s Region.

C. Modify the IAM roles of the HR departments to add a data filter for each department’s Region. This option is not possible, as data filters are not added to IAM roles, but to permissions granted by AWS Lake Formation. Moreover, this option does not specify how to register the S3 path as a data lake location, or how to enable fine-grained access control in AWS Lake Formation.

E. Create a separate S3 bucket for each Region. Configure an IAM policy to allow S3 access. Restrict access based on Region. This option is not recommended, as it would require more operational overhead to create and manage multiple S3 buckets, and to configure and maintain IAM policies for each HR department. Moreover, this option does not leverage the benefits of AWS Lake Formation, such as data cataloging, data transformation, and data governance.

1: AWS Lake Formation

2: AWS Lake Formation Permissions

AWS Identity and Access Management

Amazon S3