MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A financial institution uses SageMaker to train and deploy models. They need to track every experiment, model version, and deployment step for audit purposes. Which SageMaker feature should they use to capture the full lineage of artifacts, actions, and contexts?
⚠ Common exam trap
MLA-C01 often tests the confusion between SageMaker features, leading candidates to select Model Registry or Experiments when full lineage tracking across all artifacts is required.
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 feature designed to capture the full lineage of artifacts, actions, and contexts, providing an end-to-end audit trail of the machine learning workflow. It automatically records relationships between data, models, and experiments.
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 Clarify
Why it's wrong here
Clarify detects bias and explains model predictions; it does not record lineage. The requirement is SageMaker ML Lineage Tracking, which stores artifacts, actions and contexts as a queryable graph for audit. Clarify would be chosen when the task is fairness analysis or feature attribution.
- ✗
SageMaker Model Registry
Why it's wrong here
Model Registry catalogues model versions and their approval status, but it captures only models, not the full graph of artifacts, actions and contexts across training and deployment. ML Lineage Tracking provides that end-to-end audit trail. Registry suits version promotion and approval workflows.
- ✗
SageMaker Experiments
Why it's wrong here
SageMaker Experiments groups runs and trials for comparison, but it does not build a lineage graph of artifacts, actions and contexts. The correct feature is SageMaker ML Lineage Tracking, which records those entities and their relationships for audit. Experiments suits model tuning and run comparison.
- ✓
SageMaker ML Lineage Tracking
Why this is correct
SageMaker ML Lineage Tracking automatically records relationships among artifacts, actions and contexts across training and deployment, giving the auditable end-to-end history the institution requires. Experiment tracking alone does not capture deployment steps or cross-resource lineage.
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Same concept, more angles
1 more way this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A machine learning engineer deploys a multi-model endpoint using SageMaker. They need to track which model version was used for each inference request for compliance purposes. Which service should they integrate to capture this lineage?
hard- A.AWS CloudTrail
- B.SageMaker Model Monitor
- ✓ C.SageMaker ML Lineage Tracking
- D.Amazon DynamoDB with custom logging
Why C: SageMaker ML Lineage Tracking is the correct service because it is specifically designed to capture and query the lineage of machine learning artifacts, including model versions, datasets, and inference requests. By integrating with SageMaker endpoints, it automatically records the model version used for each inference, enabling compliance auditing without custom code.
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.