MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A data scientist wants to train a model on SageMaker using a custom PyTorch script, then register the best model in the SageMaker Model Registry. The training job is part of a SageMaker Pipeline. Which pipeline step should be used to register the model?
⚠ Common exam trap
Candidates often confuse `CreateModelStep` (which creates a deployable model resource) with `RegisterModelStep` (which creates a model package version in the registry), assuming both serve the same purpose of model registration.
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
✓
RegisterModelStep
The `RegisterModelStep` is specifically designed to create a model resource and register it in the SageMaker Model Registry as part of a pipeline. It takes the training output (e.g., model artifacts from a `TrainingStep`) and packages it with the specified inference image and metadata, then creates a model package group version. This is the correct step for registering a model after training, as it directly integrates with the Model Registry for versioning and approval workflows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
RegisterModelStep
Why this is correct
RegisterModelStep registers a trained model into the Model Registry.
- ✗
CreateModelStep
Why it's wrong here
CreateModelStep creates a SageMaker Model object but does not register it in the registry.
- ✗
TrainingStep
Why it's wrong here
TrainingStep only trains; it doesn't register the model.
- ✗
TransformStep
Why it's wrong here
TransformStep runs batch inference, not model registration.
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