MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company uses SageMaker Pipelines to automate their ML workflow. They need to add model versioning and approval workflow. Which THREE steps should they include in their pipeline to achieve this? (Choose THREE.)
⚠ Common exam trap
Many exam-takers think the Training step alone suffices for versioning, but AWS explicitly separates model training from model registration, requiring the RegisterModel step for registry integration.
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
✓
RegisterModel step
The RegisterModel step is correct because it creates a model package in SageMaker Model Registry, which enables versioning and approval workflows. This step registers the trained model artifact along with metadata, allowing the pipeline to track model versions and trigger approval processes for deployment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
RegisterModel step
Why this is correct
This step creates a new model version in the Model Registry.
- ✗
Training step
Why it's wrong here
Training step only trains the model; model registration requires a RegisterModel step.
- ✓
Condition step
Why this is correct
A Condition step can check if metrics meet a threshold and route to the RegisterModel step only if conditions are met.
- ✓
Processing step for evaluation
Why this is correct
A Processing step can compute evaluation metrics to determine if the model should be registered.
- ✗
Transform step
Why it's wrong here
Transform step runs batch inference and does not contribute to model versioning.
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