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Answer: Amazon Redshift with Redshift ML
Amazon Redshift ML is a feature of Amazon Redshift that allows you to run machine learning models directly on the data stored in the data warehouse. It can be used to optimize for machine learning workloads by enabling fast and scalable data processing and analytics. To configure it, you can create a Redshift ML model, define the necessary input and output data, and specify the machine learning algorithm to use. Amazon S3 with S3 Select can be used for querying data stored in S3, but it is not optimized for machine learning workloads. Amazon RDS with read replicas can improve read performance, but it is not designed for machine learning workloads. Amazon DynamoDB with DynamoDB Accelerator (DAX) can be used for caching and accelerating read access to DynamoDB tables, but it is not optimized for machine learning workloads.
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Your company is developing a machine learning application that requires large-scale data processing and analytics. Which AWS storage service should you use, and how should you configure it to optimize for machine learning workloads?
A
Amazon S3 with S3 Select
B
Amazon RDS with read replicas
C
Amazon DynamoDB with DynamoDB Accelerator (DAX)
D
Amazon Redshift with Redshift ML