- A
SageMaker Model Registry.
Model Registry provides version control for models and supports approval workflows for deployment.
- B
SageMaker Experiments.
Why wrong: SageMaker Experiments tracks training runs and parameters, but does not manage model versions or approvals.
- C
SageMaker Feature Store.
Why wrong: Feature Store manages features for training and inference, not model versions.
- D
SageMaker Ground Truth.
Why wrong: Ground Truth is a data labeling service, not for model management.
Question 869 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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 data scientist wants to version control trained models and manage approvals for deployment. Which SageMaker feature should they use?
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 Model Registry.
SageMaker Model Registry is the correct feature because it is specifically designed for versioning trained models, tracking their metadata (e.g., training job, metrics), and managing approval workflows for deployment stages (e.g., Pending, Approved, Rejected). This directly addresses the data scientist's need to version control models and manage deployment approvals.
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.
- ✓
SageMaker Model Registry.
Why this is correct
Model Registry provides version control for models and supports approval workflows for deployment.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
SageMaker Experiments.
Why it's wrong here
SageMaker Experiments tracks training runs and parameters, but does not manage model versions or approvals.
- ✗
SageMaker Feature Store.
Why it's wrong here
Feature Store manages features for training and inference, not model versions.
- ✗
SageMaker Ground Truth.
Why it's wrong here
Ground Truth is a data labeling service, not for model management.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse SageMaker Experiments (which tracks training runs) with the Model Registry (which manages model versions and approvals), leading them to pick Experiments because they think 'version control' refers to experiment iterations rather than model artifacts.
Detailed technical explanation
How to think about this question
Under the hood, the Model Registry uses a model package group to organize model versions, each with a unique version number and associated metadata (e.g., training job ARN, inference specification). Approval status transitions (e.g., from Pending to Approved) can trigger automated deployment pipelines via AWS Step Functions or SageMaker Pipelines, enabling governance and audit trails. A real-world scenario is a team using the registry to enforce that only models with an 'Approved' status are deployed to production, preventing accidental rollouts of unvalidated models.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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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: SageMaker Model Registry. — SageMaker Model Registry is the correct feature because it is specifically designed for versioning trained models, tracking their metadata (e.g., training job, metrics), and managing approval workflows for deployment stages (e.g., Pending, Approved, Rejected). This directly addresses the data scientist's need to version control models and manage deployment approvals.
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.
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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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