- A
Set the model version status to 'Approved' in the Model Registry
Only model versions with Approved status can be deployed via SageMaker endpoints.
- B
Tag the model version as 'production-ready'
Why wrong: Tags do not enforce deployment restrictions.
- C
Manually move the model artifact to a production S3 bucket
Why wrong: This bypasses the registry and does not enforce approval.
- D
Use AWS IAM policies to restrict deployment to specific model ARNs
Why wrong: While IAM can restrict, it does not integrate with the approval workflow.
Question 660 of 1,000
MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
This MLA-C01 practice question tests your understanding of deployment and orchestration of ml workflows. 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 financial services company needs to enforce that only approved model versions are deployed to production. They use SageMaker Model Registry to track versions, with an approval workflow. Which action must they take in the model registry to ensure only approved models can be deployed?
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
✓
Set the model version status to 'Approved' in the Model Registry
Option A is correct because the SageMaker Model Registry uses a status field to control the lifecycle of model versions. By setting the model version status to 'Approved', the company can enforce that only approved models are deployable, as SageMaker's deployment APIs (e.g., CreateModel, CreateEndpointConfig) can be configured to require an 'Approved' status. This integrates with the approval workflow, ensuring that unapproved or pending versions are blocked from production deployment.
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.
- ✓
Set the model version status to 'Approved' in the Model Registry
Why this is correct
Only model versions with Approved status can be deployed via SageMaker endpoints.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Tag the model version as 'production-ready'
Why it's wrong here
Tags do not enforce deployment restrictions.
- ✗
Manually move the model artifact to a production S3 bucket
Why it's wrong here
This bypasses the registry and does not enforce approval.
- ✗
Use AWS IAM policies to restrict deployment to specific model ARNs
Why it's wrong here
While IAM can restrict, it does not integrate with the approval workflow.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse tagging (a flexible but non-enforceable mechanism) with the Model Registry's built-in approval status, which is specifically designed to enforce deployment gates in SageMaker.
Detailed technical explanation
How to think about this question
Under the hood, the SageMaker Model Registry stores model versions in a PackageGroup, and each version has a 'ModelApprovalStatus' attribute that can be 'PendingManualApproval', 'Approved', or 'Rejected'. When deploying, you can use the 'Approved' status as a condition in IAM policies via the 'sagemaker:ModelApprovalStatus' condition key, or use the registry's built-in deployment guardrails. In a real-world scenario, a CI/CD pipeline can automatically deploy only versions with 'Approved' status, preventing accidental deployment 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 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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Deployment and Orchestration of ML Workflows — This question tests Deployment and Orchestration of ML Workflows — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set the model version status to 'Approved' in the Model Registry — Option A is correct because the SageMaker Model Registry uses a status field to control the lifecycle of model versions. By setting the model version status to 'Approved', the company can enforce that only approved models are deployable, as SageMaker's deployment APIs (e.g., CreateModel, CreateEndpointConfig) can be configured to require an 'Approved' status. This integrates with the approval workflow, ensuring that unapproved or pending versions are blocked from production deployment.
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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