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 stores and shares features but does not track model training lineage.
- ✓
SageMaker ML Lineage Tracking
Why this is correct
ML Lineage Tracking creates a directed acyclic graph of artifacts, actions, and contexts, enabling full reproducibility and audit trails.
- ✗
SageMaker Experiments
Why it's wrong here
SageMaker Experiments tracks trials and parameters but does not automatically link artifacts and actions across the ML lifecycle.
- ✗
SageMaker Model Registry
Why it's wrong here
Model Registry manages model versions and approvals but does not track the training pipeline lineage.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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