MLS-C01 Practice Question: Machine Learning Implementation and Operations
A machine learning engineer is designing an automated ML pipeline for training and deploying models. The pipeline must include data validation, model training, hyperparameter tuning, and model deployment. The engineer wants to use AWS services that integrate well and provide version control. Which THREE services should be combined to achieve this? (Choose THREE.)
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between orchestration services (Step Functions) and data processing services (Glue, EMR), leading candidates to mistakenly choose Glue for pipeline orchestration because of its 'data validation' capability, ignoring its lack of ML-specific deployment and tuning features.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AWS Step Functions
AWS Step Functions orchestrates the ML pipeline by coordinating data validation, model training, hyperparameter tuning, and deployment as a state machine. It integrates natively with Amazon SageMaker for training and tuning jobs, and with AWS CodePipeline for CI/CD, enabling version control and automated transitions between pipeline stages.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Glue
Why it's wrong here
Wrong: Glue is for ETL, not pipeline orchestration.
- ✓
AWS Step Functions
Why this is correct
Correct: Orchestrates the ML pipeline steps.
- ✓
AWS CodePipeline
Why this is correct
Correct: Manages CI/CD and version control for the pipeline.
- ✗
Amazon EMR
Why it's wrong here
Wrong: EMR is for big data processing, not ML pipeline management.
- ✓
Amazon SageMaker
Why this is correct
Correct: Provides training, tuning, and deployment capabilities.
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