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…

Question

Options

• AWrite a direct connection to the SQL database within the notebook and pull data in
• BPush the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide
• CMove the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook
• DMove the data to Amazon ElastiCache using AWS DMS and set up a connection within the

• A
  7% (2)
• B
  75% (21)
• C
  4% (1)
• D
  14% (4)

Explanation

Amazon SageMaker's training jobs are designed to consume data from Amazon S3. SageMaker cannot natively connect to Amazon RDS during training. The correct pattern is to extract data from RDS and stage it in S3, then point SageMaker at the S3 location. AWS Data Pipeline (or AWS Glue/DMS) can automate the RDS-to-S3 export. Option A (notebook SQL connection) is brittle and inefficient for large datasets; notebooks are for experimentation, not production pipelines. Option C (DynamoDB) adds unnecessary complexity - DynamoDB is a NoSQL key-value store not suited for ML training data staging. Option D (ElastiCache) is an in-memory caching layer, entirely inappropriate for persisting training datasets.

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