- A
Amazon S3 versioning
Versioning enables tracking changes to datasets and model artifacts over time.
- B
SageMaker Experiments
Tracks trials, parameters, and metrics for model development.
- C
AWS CloudTrail
Why wrong: CloudTrail logs API calls but does not provide ML-specific lineage relationships.
- D
SageMaker ML Lineage Tracking
Core service for tracking artifacts, actions, and contexts.
- E
AWS Config
Why wrong: AWS Config tracks resource configuration changes but not ML lineage.
Question 651 of 1,000
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance, and security. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.)
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 is correct because it preserves every version of an object stored in an S3 bucket, including model artifacts, datasets, and configuration files. By enabling versioning, you can retrieve and revert to any previous version of a model artifact, which is essential for reproducibility and auditability. This directly supports tracking the lineage of ML models by ensuring that the exact input data and model binaries used in a specific experiment are never overwritten or lost.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Versioning enables tracking changes to datasets and model artifacts over time.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
SageMaker Experiments
Why this is correct
Tracks trials, parameters, and metrics for model development.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AWS CloudTrail
Why it's wrong here
CloudTrail logs API calls but does not provide ML-specific lineage relationships.
- ✓
SageMaker ML Lineage Tracking
Why this is correct
Core service for tracking artifacts, actions, and contexts.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
AWS Config
Why it's wrong here
AWS Config tracks resource configuration changes but not ML lineage.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
SageMaker ML Lineage Tracking creates a directed acyclic graph (DAG) of entities such as datasets, training jobs, models, and endpoints, storing relationships in a graph database. SageMaker Experiments groups related trials and tracks parameters, metrics, and artifacts, while S3 versioning provides immutable storage for each artifact version. Together, they enable full end-to-end traceability: from raw data (S3 versioned) through training (Experiment trial) to the final model artifact (Lineage Tracking node).
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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ML Solution Monitoring, Maintenance, and Security — study guide chapter
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Solution Monitoring, Maintenance, and Security — This question tests ML Solution Monitoring, Maintenance, and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Amazon S3 versioning — Amazon S3 versioning is correct because it preserves every version of an object stored in an S3 bucket, including model artifacts, datasets, and configuration files. By enabling versioning, you can retrieve and revert to any previous version of a model artifact, which is essential for reproducibility and auditability. This directly supports tracking the lineage of ML models by ensuring that the exact input data and model binaries used in a specific experiment are never overwritten or lost.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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