An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets. Which solution will meet these requirements?

Question

Options

• AUse Amazon Data Firehose to create the data ingestion pipelines. Use Amazon SageMaker
• BUse AWS Glue to create the data ingestion pipelines. Use Amazon SageMaker Studio Classic to
• CUse Amazon Redshift ML to create the data ingestion pipelines. Use Amazon SageMaker Studio
• DUse Amazon Athena to create the data ingestion pipelines. Use an Amazon SageMaker notebook

• A
  5% (2)
• B
  77% (30)
• C
  13% (5)
• D
  5% (2)

Explanation

Option B is correct because AWS Glue is a fully managed ETL service purpose-built for creating data ingestion and transformation pipelines from sources like S3, making it the natural fit for the first requirement. Amazon SageMaker Studio Classic provides a complete, integrated IDE for the full ML lifecycle - including model training, packaging, and deployment - which directly satisfies the deployment pipeline requirement.

Why the distractors fail:

• A (Data Firehose): Firehose is a real-time streaming delivery service (e.g., logs → S3/Redshift), not a tool for building ingestion pipelines from existing S3 data.
• C (Redshift ML): Redshift ML allows training models inside Redshift via SQL - it's not a data ingestion pipeline tool, and it introduces an unnecessary data warehouse layer.
• D (Athena): Athena is an interactive query service for analyzing data already in S3; it has no pipeline orchestration or ingestion capabilities.

Memory tip: Think of it as two distinct jobs - Glue moves and transforms data (ingestion), SageMaker Studio builds and deploys models (ML pipelines). If the question mentions S3 raw data + ML deployment, "Glue → SageMaker Studio" is almost always the AWS-native answer.

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