The answer is SageMaker Model Registry. This is the correct choice because it is purpose-built for managing model versions and controlling the approval workflow for deployment, allowing data scientists to register, track, and promote models through stages like pending, approved, or rejected. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your ability to distinguish between SageMaker’s core services: while SageMaker Experiments tracks training runs, and Feature Store or Ground Truth handle data and labeling, Model Registry is the only service that combines versioning with a structured approval pipeline. A common trap is confusing it with SageMaker Experiments, but remember that Experiments is for logging parameters and metrics, not for deployment governance. To lock in the concept, use the memory tip: “Registry for release, Experiments for exploration.”
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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
SageMaker Model Registry.
Option B is correct because SageMaker Model Registry is purpose-built for model versioning and approval workflows. Option A is for experiment tracking, not deployment. Option C is for labeling data. Option D is for feature storage.
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
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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.
What to study next
Got this wrong? Here's your next step.
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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. — Option B is correct because SageMaker Model Registry is purpose-built for model versioning and approval workflows. Option A is for experiment tracking, not deployment. Option C is for labeling data. Option D is for feature storage.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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