- A
Manually review each model before deployment
Why wrong: Manual review is error-prone and not scalable; it does not enforce the policy in code.
- B
Use SageMaker Model Monitor to check model quality after deployment
Why wrong: Model Monitor monitors deployed models, but does not prevent deployment of unapproved models.
- C
Use IAM policies to restrict deployment to only Approved model versions
IAM policies can be written to allow SageMaker CreateEndpoint only for models with an Approved approval status, which is best practice.
- D
Store model metadata in a DynamoDB table and check it before deployment
Why wrong: While possible, this is a custom solution; the native approval workflow in Model Registry is simpler and directly supported.
Question 611 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. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
An organization wants to ensure that only approved model versions can be deployed to production. They use the SageMaker Model Registry to track model versions. How can they enforce that only approved models are 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
✓
Use IAM policies to restrict deployment to only Approved model versions
Option C is correct because AWS IAM policies can be used to conditionally restrict SageMaker API actions (e.g., CreateEndpointConfig, CreateModel) based on the model version's approval status. By evaluating the `sagemaker:ModelPackageApprovalStatus` condition key in an IAM policy, you can enforce that only model versions with an `Approved` status can be deployed, providing a native, automated, and auditable enforcement mechanism without manual intervention or external dependencies.
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.
- ✗
Manually review each model before deployment
Why it's wrong here
Manual review is error-prone and not scalable; it does not enforce the policy in code.
- ✗
Use SageMaker Model Monitor to check model quality after deployment
Why it's wrong here
Model Monitor monitors deployed models, but does not prevent deployment of unapproved models.
- ✓
Use IAM policies to restrict deployment to only Approved model versions
Why this is correct
IAM policies can be written to allow SageMaker CreateEndpoint only for models with an Approved approval status, which is best practice.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Store model metadata in a DynamoDB table and check it before deployment
Why it's wrong here
While possible, this is a custom solution; the native approval workflow in Model Registry is simpler and directly supported.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse SageMaker Model Monitor (post-deployment monitoring) with pre-deployment approval enforcement, or they assume custom external checks (DynamoDB) are necessary when SageMaker provides native IAM-based conditional enforcement.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Model Registry assigns each model version a `ModelPackageApprovalStatus` that can be `PendingManualApproval`, `Approved`, or `Rejected`. IAM policies can use the `sagemaker:ModelPackageApprovalStatus` condition key to allow `sagemaker:CreateModel` only when the status equals `Approved`. This approach integrates with AWS CloudTrail for auditing and can be combined with AWS Organizations SCPs for multi-account governance. A real-world scenario is a regulated healthcare pipeline where only FDA-cleared model versions must be deployed; IAM enforcement prevents accidental rollouts of unapproved versions even if CI/CD scripts are misconfigured.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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: Use IAM policies to restrict deployment to only Approved model versions — Option C is correct because AWS IAM policies can be used to conditionally restrict SageMaker API actions (e.g., CreateEndpointConfig, CreateModel) based on the model version's approval status. By evaluating the `sagemaker:ModelPackageApprovalStatus` condition key in an IAM policy, you can enforce that only model versions with an `Approved` status can be deployed, providing a native, automated, and auditable enforcement mechanism without manual intervention or external dependencies.
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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