You work as a machine learning specialist in a financial services company in their asset management division. You have completed a pilot of a Random Cut Forest algorithm-based model that you plan to use to find anomalies in your trading data. You have tested your model using a small data sample and are ready to implement your production model using SageMaker. The historical trading data that you will use for training is stored in an RDS Microsoft SQL Server database. How should you prepare your historical trading data to train your production model?

Answer options:

A.Use the DMS service to move the data to ElastiCache, then connect your SageMaker notebook with ElastiCache to load the data at low latency.
B.Use a Lambda function to load the data to DynamoDB tables, then connect your SageMaker notebook to DynamoDB to load your trading data.
C.Use the Data Pipeline service to move your trading data from the Microsoft SQL Server database to S3, then use the S3 bucket within your SageMaker notebook to load your trading data.
D.Directly connect to the SQL database using Direct Connect from your SageMaker notebook and load the trading data.

Correct Answer: C
Option A is incorrect. You cannot load data directly from ElastiCache into a SageMaker notebook.
Option B is incorrect. You cannot load data into a SageMaker notebook directly from DynamoDB without first staging the data in S3.
Option C is correct. Loading your training data from S3 is the best approach for getting your data into your SageMaker notebook. Also, the Data Pipeline service is the preferred method of loading data from Microsoft SQL Server to S3.
Option D is incorrect. You cannot directly load data from an RDS instance into a SageMaker notebook instance without first staging the data in S3.
References:
Please see the AWS Re:Invent 2018 presentation titled Train Models on Amazon SageMaker Using Data Not from Amazon S3 (AIM419) - AWS re:Invent 2018 (https://www.slideshare.net/AmazonWebServices/train-models-on-amazon-sagemaker-using-data-not-from-amazon-s3-aim419-aws-reinvent-2018),
The AWS SageMaker developer guide titled Random Cut Forest (RCF) Algorithm (https://docs.aws.amazon.com/sagemaker/latest/dg/randomcutforest.html),
The AWS Data Pipeline developer guide titled What is AWS Data Pipeline? (https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/what-is-datapipeline.html),
The Amazon Amazon SageMaker developer guide titled Download, Prepare, and Upload Training Data (https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-ex-data.html)