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Deployment and Orchestration of ML WorkflowsmediumMultiple ChoiceObjective-mapped

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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Written by Johnson Ajibi, MSc IT Security

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