MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A data science team wants to track the lineage of models, including datasets, training jobs, and endpoints, for reproducibility and audit. They need a solution that captures relationships between artifacts automatically during training and deployment. Which service should they use?
⚠ Common exam trap
MLA-C01 often tests the distinction between lineage tracking and model registry; candidates pick Model Registry because it sounds like governance, but it only catalogs models, not their data ancestry.
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 automatically captures relationships among datasets, training jobs, model artifacts, and endpoints as the pipeline runs, producing a queryable lineage graph for reproducibility and audit. It records entities and associations without custom instrumentation, which is exactly what the team needs. SageMaker Experiments tracks runs and metrics but does not build the full artifact relationship graph.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon S3 object versioning
Why it's wrong here
S3 object versioning retains multiple revisions of individual objects, but it records no relationships between datasets, training jobs and endpoints. It is tempting because it preserves data history, yet SageMaker Lineage Tracking is what captures those cross-artefact dependencies automatically.
- ✗
SageMaker Experiments
Why it's wrong here
SageMaker Experiments groups runs and metrics for comparison, but it does not automatically record dataset-to-job-to-endpoint lineage. It is tempting because it tracks training trials, yet SageMaker Lineage Tracking is the service that builds the artefact relationship graph for audit and reproducibility.
- ✗
SageMaker Model Registry
Why it's wrong here
Model Registry catalogues model versions and approval status, but it does not automatically capture dataset-to-training-job-to-endpoint relationships. It is tempting as a model store, yet SageMaker Lineage Tracking builds that artefact graph automatically during training and deployment.
- ✓
SageMaker ML Lineage Tracking
Why this is correct
SageMaker ML Lineage Tracking automatically records relationships between datasets, training jobs, model artefacts, and endpoints as they are created, forming a queryable lineage graph. This satisfies the requirement for automatic capture during training and deployment for reproducibility and audit.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.