MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A healthcare analytics team trains models in SageMaker and stores artifacts in an S3 bucket that contains protected health information. An auditor asks how the team can prove which training dataset and container image produced the model currently deployed to production, and wants the evidence retained even if someone deletes the training job. Which SageMaker capability should the team rely on to capture and retain this metadata automatically?
⚠ Common exam trap
The trap here is assuming Model Registry or Experiments captures full provenance automatically, when lineage is the service that records dataset-to-model associations and retains them independently of the training job.
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, which automatically records entities and associations such as datasets, training jobs, and model artifacts as the workflow runs.
ML Lineage Tracking automatically builds a graph of entities and associations across the ML workflow, linking datasets, training jobs, and model artifacts. This graph persists independently of the training job, so the team can trace the deployed model back to its inputs and container image even after the job is deleted, which is exactly the durable provenance an auditor needs.
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 Model Registry, which stores model versions and their approval status but does not capture dataset or container provenance automatically.
Why it's wrong here
Model Registry tracks model versions, approval workflows, and metadata you supply, but it does not automatically record the training dataset and container image lineage as the workflow executes. Unless the team manually attaches that provenance, the registry alone cannot prove which data and image produced the deployed model. It complements lineage rather than replacing it.
- ✗
SageMaker Experiments, which groups runs and logs metrics and parameters but does not persist a provenance graph after the trial components are deleted.
Why it's wrong here
SageMaker Experiments organizes training runs and records metrics, parameters, and artifacts, but its trial components are tied to the experiment run lifecycle and do not automatically form a durable provenance graph linking the deployed model to its dataset and image. It supports comparison of runs, not audit-grade lineage retention independent of job deletion.
- ✗
AWS CloudTrail management events, which record API calls such as CreateTrainingJob and CreateModel and can be queried for who performed each action.
Why it's wrong here
CloudTrail records API activity for auditing who called which action and when, but it does not capture the semantic relationships between a dataset, a training job, and the resulting model artifact. It can show that a training job was created, yet it cannot reconstruct which data and container produced the deployed model, so it does not meet the provenance requirement.
- ✓
SageMaker ML Lineage Tracking, which automatically records entities and associations such as datasets, training jobs, and model artifacts as the workflow runs.
Why this is correct
ML Lineage Tracking automatically creates entities and associations for data, training jobs, and models, forming a queryable graph that links the deployed model back to its training dataset and container image. Because the lineage graph is retained independently of the training job's lifecycle, it provides durable evidence for the audit even if the job is deleted, satisfying the reproducibility requirement.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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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.