Q36 — AWS SAA-C03 Ch.12

Question 36 of 100 | ← Chapter 12

Q836. A company has an Amazon S3 data lake. The company needs a solution that transforms the data from the data lake and loads the data into a data warehouse every day. The data warehouse must have massively parallel processing (MPP) capabilities.Data analysts then need to create and train machine learning (ML) models by using SQL commands on the data. The solution must use serverless AWS services wherever possible.Which solution will meet these requirements?

Correct Answer: C. Run a daily AWS Glue job to transform the data and load the data into Amazon Redshift Serverless.Use Amazon Redshift ML to create and train the ML models.

Explanation

Apologies for the confusion in my previous response. Let's reassess the options to determine the correct solution.To meet the requirements of transforming data from an Amazon S3 data lake, loading it into a data warehouse with massively parallel processing (MPP) capabilities, and enabling machine learning (ML) model creation and training using SQL commands on the data while utilizing serverless AWS services wherever possible, the recommended solution is:C. Run a daily AWS Glue job to transform the data and load the data into Amazon Redshift Serverless. Use Amazon Redshift ML to create and train the ML models.Here's why:Option C suggests running a daily AWS Glue job to transform the data and load it into Amazon Redshift Serverless. AWS Glue is a fully managed ETL service that can process data stored in S3. By using Glue to transform the data and loading it into Redshift Serverless, you can benefit from the massively parallel processing capabilities of Redshift while eliminating the need to manage infrastructure.Amazon Redshift Serverless is a serverless variant of Amazon Redshift, which provides scalable, on-demand data warehousing without the need to provision or manage infrastructure. It allows you to scale automatically based on the workload, and you only pay for the queries you run.To create and train ML models using SQL commands on the data, you can use Amazon Redshift ML. Amazon Redshift ML is an extension of Redshift that simplifies the process of creating ML models by using SQL statements. It allows you to use SQL commands to build, train, and deploy ML models directly within Redshift.This solution leverages serverless AWS services, including AWS Glue, Amazon Redshift Serverless, and S3 for data storage. It eliminates the need to manage infrastructure, provides MPP capabilities, and allows ML model creation and training using SQL commands.Options A, B, and D are not the optimal solutions to meet the requirements with a focus on serverless AWS services.Option A suggests using Amazon EMR to transform the data and load it into Amazon Redshift. While Redshift provides MPP capabilities, this solution involves managing infrastructure with EMR, which goes against the requirement of utilizing serverless services wherever possible.Option B suggests using Amazon EMR to transform the data and load it into Amazon Aurora Serverless. However, Aurora Serverless may not provide the same level of MPP capabilities as Amazon Redshift, and the solution still involves managing infrastructure with EMR.Option D suggests running a daily AWS Glue job to transform the data and load it into Amazon Athena tables. While Athena is a serverless query service, it does not have the same MPP capabilities as Amazon Redshift. Therefore, it may not be the most optimal choice for a data warehouse requiring MPP capabilities.Therefore, the recommended solution is to run a daily AWS Glue job to transform the data and load it into Amazon Redshift Serverless. Then, utilize Amazon Redshift ML to create and train the ML models using SQL commands, ensuring a serverless approach while meeting the specified requirements.