A company has an Amazon S3 data lake. The company needs to transform the data daily and load it into a data warehouse that has massively parallel processing (MPP). Data analysts must create and train machine learning models using SQL on the data. Use serverless AWS services wherever possible. Which solution meets these requirements?

The correct solution uses AWS Glue for serverless daily data transformation and loading into Amazon Redshift Serverless, which is an MPP data warehouse and meets the serverless requirement. Redshift ML allows data analysts to create and train ML models using SQL directly within Redshift. Option A is incorrect because Amazon EMR is not a serverless service, and while Redshift is an MPP data warehouse, the question prioritizes serverless solutions. Option B is incorrect because Amazon Aurora Serverless is an OLTP database, not an MPP data warehouse, and Aurora ML is not designed for the scale of data lake analytics and ML model training as Redshift ML. Option D is incorrect because Amazon Athena is a query service for data in S3, not a data warehouse, and it lacks the MPP capabilities and dedicated ML features for SQL-based model training that Redshift ML offers.