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?

Topic #: - All AWS Certified Solutions Architect - Associate SAA-C03 Questions

A. Run a daily Amazon EMR job to transform the data and load the data into Amazon Redshift. Use Amazon Redshift ML to create and train the ML models.
B. Run a daily Amazon EMR job to transform the data and load the data into Amazon Aurora Serverless. Use Amazon Aurora ML to create and train the ML models.
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.
D. Run a daily AWS Glue job to transform the data and load the data into Amazon Athena tables. Use Amazon Athena ML to create and train the ML models.

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