MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A company uses SageMaker Model Registry to manage model versions. They want to enforce that only models with an 'Approved' status can be deployed to production endpoints. How can they enforce this?
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 IAM policies with a condition on sagemaker:ModelVersionStatus
SageMaker Model Registry supports approval workflows. By using IAM policies that conditionally allow deployment only when the model version status is 'Approved', the company can enforce governance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use AWS Lambda to check the model status during deployment
Why it's wrong here
Lambda can check but doesn't enforce at the IAM level; a determined user could bypass.
- ✓
Set IAM policies with a condition on sagemaker:ModelVersionStatus
Why this is correct
IAM condition keys allow restricting CreateEndpointConfig to only approved models.
- ✗
Use SageMaker Pipelines to deploy only approved models
Why it's wrong here
Pipelines can include a ConditionStep, but IAM enforcement is more robust and prevents manual deployments.
- ✗
Configure SCPs to block deployment of unapproved models
Why it's wrong here
SCPs are organizational policies, not granular to model status.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.