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.
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SageMaker Model Registry
Why it's wrong here
Model Registry catalogues model versions and approval status for deployment governance, without capturing dataset or training-job relationships. It is correct when promoting and versioning models, but the lineage graph of artifacts and actions requires a different feature.
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SageMaker Experiments
Why it's wrong here
SageMaker Experiments groups runs and trials for comparing metrics and parameters across training attempts; it does not model datasets, models and jobs as lineage artifacts and actions. It is the right choice when tracking experiment performance, not provenance relationships.
- ✓
SageMaker ML Lineage Tracking
Why this is correct
SageMaker ML Lineage Tracking automatically records relationships between datasets, training jobs and model artifacts as lineage entities, capturing both artifacts and actions. This directly satisfies the stem's requirement to track provenance across the machine learning workflow, which generic logging or experiment tracking alone would not provide.
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SageMaker Feature Store
Why it's wrong here
Feature Store centralises engineered feature definitions for reuse between training and inference; it stores feature values, not lineage relationships between datasets, models and jobs. It is correct when preventing training-serving skew, not for provenance tracking.
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