MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A machine learning team trains a model in SageMaker and wants to track every step — from dataset version to hyperparameters to final model artifact — for reproducibility and audit compliance. Which SageMaker feature should they use?
⚠ Common exam trap
Candidates often confuse SageMaker Experiments (which tracks trial metrics and parameters) with ML Lineage Tracking (which captures the full end-to-end provenance graph), leading them to pick Experiments when the question explicitly asks for tracking every step from dataset to final artifact for audit compliance.
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 is the correct choice because it is specifically designed to create a directed acyclic graph (DAG) of every step in the ML workflow, including dataset versions, hyperparameters, training jobs, and model artifacts. This enables full reproducibility and audit compliance by capturing the provenance of each entity and their relationships, which is exactly what the question requires.
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 Feature Store
Why it's wrong here
Feature Store manages curated feature groups for training and online inference serving, not run-level lineage. It records feature definitions and ingestion timestamps, but not hyperparameters or model artifacts, so the audit trail the stem demands cannot be reconstructed from it.
- ✓
SageMaker ML Lineage Tracking
Why this is correct
ML Lineage Tracking automatically captures entities and artefacts across the ML workflow, recording dataset versions, hyperparameters and model artefacts as a queryable graph. This satisfies the reproducibility and audit compliance constraint by preserving end-to-end traceability.
- ✗
SageMaker Experiments
Why it's wrong here
Experiments groups training runs and logs metrics, parameters and artifacts for comparison, yet it does not itself version the source dataset or provide the end-to-end lineage chain. It is the right tool when comparing many training trials, not for dataset-to-artifact audit tracking.
- ✗
SageMaker Model Registry
Why it's wrong here
Model Registry catalogues trained model versions and their approval status for deployment governance. It begins after training completes, so dataset versions and hyperparameters used during each run are not captured, leaving the reproducibility chain the stem requires incomplete.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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