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MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security

A data scientist wants to track the lineage of models, datasets, and training jobs in SageMaker. Which SageMaker feature should they use to capture these relationships as artifacts and actions?

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

SageMaker ML Lineage Tracking

SageMaker ML Lineage Tracking creates a graph of artifacts (datasets, models) and actions (training jobs, endpoints) to track the provenance of ML 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.

  • SageMaker Model Registry

    Why it's wrong here

    Model Registry manages model versions and approvals but does not track the full lineage of datasets and training jobs.

  • SageMaker Experiments

    Why it's wrong here

    Experiments track trial parameters and metrics but do not create a lineage graph of artifacts and actions.

  • SageMaker ML Lineage Tracking

    Why this is correct

    Lineage Tracking explicitly models artifacts, actions, and contexts to provide end-to-end reproducibility.

  • SageMaker Feature Store

    Why it's wrong here

    Feature Store manages feature definitions and offline/online storage, not lineage of ML artifacts.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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