MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company wants to track the lineage of their ML models for reproducibility and auditability. Which THREE services or features should they use together to achieve this? (Choose THREE.)
⚠ Common exam trap
Many exam-takers confuse AWS CloudTrail or AWS Config with lineage tracking because both deal with 'tracking' and 'auditing,' but they operate at the infrastructure/API level, not at the ML experiment and artifact relationship level required for model lineage.
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
✓
Amazon S3 versioning
Amazon S3 versioning (A) is correct because it preserves every version of the datasets and model artifacts stored in S3, so a given training run can be tied to the exact immutable object version used, which is essential for reproducibility and auditability. SageMaker Experiments (B) is correct because it records experiment runs, trial components, parameters, metrics, and input/output artifacts, giving the structured record of each training attempt needed to reproduce results. SageMaker ML Lineage Tracking (D) is correct because it automatically creates and stores entities and relationships (trials, trial components, artifacts, contexts, actions) forming a queryable lineage graph from data through training to the deployed model. AWS CloudTrail (C) only logs API activity and control-plane events for auditing who did what, not the data/model lineage relationships, so it does not by itself provide reproducibility lineage. AWS Config (E) evaluates and records resource configuration compliance over time, which is unrelated to tracking ML artifact provenance and experiment history.
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 versioning
Why this is correct
Amazon S3 versioning preserves every object revision, so each training dataset and model artefact retains an immutable, retrievable history. This satisfies the lineage and auditability constraint by preventing overwrites, letting auditors trace exactly which data version produced a given model. Combined with SageMaker ML Lineage Tracking and Model Registry, it completes the reproducibility requirement.
- ✓
SageMaker Experiments
Why this is correct
SageMaker Experiments captures runs, parameters, metrics and artefacts, giving the trial-and-error lineage needed for reproducibility. It records each training attempt against its inputs, satisfying the auditability constraint by linking models to the exact configuration and dataset versions that produced them.
- ✗
AWS CloudTrail
Why it's wrong here
CloudTrail records API activity and account events, not the data, artefacts and parameters that produced a model. It is tempting because auditability sounds like CloudTrail's purpose, but model lineage requires SageMaker ML Lineage Tracking, Model Registry and experiment tracking metadata instead.
- ✓
SageMaker ML Lineage Tracking
Why this is correct
SageMaker ML Lineage Tracking automatically captures relationships between datasets, training jobs, model artefacts and endpoints, forming a queryable lineage graph. This directly satisfies the reproducibility and auditability requirement, since every experiment's inputs, outputs and transformations are recorded as entities and associations without manual logging.
- ✗
AWS Config
Why it's wrong here
AWS Config tracks resource configuration changes and compliance, not the training data, hyperparameters or artefacts behind a model. It is tempting because audit and governance overlap with lineage, but reproducibility needs SageMaker ML Lineage Tracking, Model Registry and experiment tracking rather than configuration history.
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 |
Go deeper
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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.