Which approach should the Specialist use for training a model using that data?

A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker. The historical training data is stored in Amazon RDS

Which approach should the Specialist use for training a model using that data?
A . Write a direct connection to the SQL database within the notebook and pull data in
B . Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
C . Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook
to pull data in
D . Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.

View Answer

Answer: B

Explanation:

Pushing the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and providing the S3 location within the notebook is the best approach for training a model using the data stored in Amazon RDS. This is because Amazon SageMaker can directly access data from Amazon S3 and train models on it. AWS Data Pipeline is a service that can automate the movement and transformation of data between different AWS services. It can also use Amazon RDS as a data source and Amazon S3 as a data destination. This way, the data can be transferred efficiently and securely without writing any code within the notebook.

References:

Amazon SageMaker

AWS Data Pipeline