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?
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 creates a graph of artifacts (datasets, models), actions (training jobs, deployment), and contexts (experiments). It provides a complete audit trail. Experiments alone track trials but not lineage. Model Registry tracks model versions but not full pipeline lineage. Clarify is for bias monitoring.
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 provides bias detection and explainability, not lineage tracking.
- ✗
SageMaker Model Registry
Why it's wrong here
Model Registry manages model versions and metadata, but not lineage of other artifacts.
- ✗
SageMaker Experiments
Why it's wrong here
Experiments track trial parameters and metrics, but not full artifact lineage.
- ✓
SageMaker ML Lineage Tracking
Why this is correct
Lineage Tracking records relationships between all ML steps for auditability.
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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 by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.