MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A data science team uses SageMaker to train and deploy models. They need to track model lineage, including datasets, training jobs, and model versions, to ensure reproducibility. Which THREE actions should they take? (Select THREE)
⚠ Common exam trap
Test-takers frequently confuse SageMaker Experiments (which tracks metrics and parameters) with ML Lineage Tracking (which tracks the full provenance graph), leading them to select D instead of A, even though Experiments alone does not capture the inter-resource relationships needed for reproducibility.
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
✓
Enable SageMaker ML Lineage Tracking
A is correct because SageMaker ML Lineage Tracking automatically captures the relationships between datasets, training jobs, and model versions, creating a directed acyclic graph (DAG) of the ML workflow. This enables full reproducibility by allowing you to trace which data and code produced a specific model, without manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable SageMaker ML Lineage Tracking
Why this is correct
Lineage tracking automatically records artifacts, actions, and contexts.
- ✓
Register all models in the SageMaker Model Registry
Why this is correct
Model Registry tracks model versions and metadata.
- ✗
Store trained models in a public S3 bucket
Why it's wrong here
Public access is insecure and unnecessary for reproducibility.
- ✗
Use SageMaker Experiments to organize training runs
Why it's wrong here
Experiments track runs but are not the primary tool for lineage across full ML lifecycle.
- ✓
Tag all resources with metadata such as project ID and training run ID
Why this is correct
Tags help associate resources with lineage and improve searchability.
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Written by Johnson Ajibi, MSc IT Security
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