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MLA-C01 Practice Question: Using SageMaker Model Registry to manage model…
A company is using SageMaker Model Registry to manage model versions. They want to automatically deploy the latest approved model to production after retraining. Which approach is best?
⚠ Common exam trap
Candidates often choose Option B because it sounds automated, but they overlook the critical requirement for model approval before deployment, which Lambda alone cannot enforce without additional logic.
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
✓
Create a SageMaker Pipeline that includes a model approval step and deployment step
A SageMaker Pipeline can orchestrate the entire workflow from retraining to deployment, including a model approval step that gates deployment to production only when the model is approved. This automates the process end-to-end, ensuring that only approved models are deployed, which aligns with the requirement to automatically deploy the latest approved model after retraining.
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 deploy the approved model using the SageMaker console
Why it's wrong here
Manual console deployment contradicts the requirement for automatic deployment after retraining, since a person must notice the approval and act. Automation via Model Registry approval events and a deployment pipeline is needed. Manual approval and deployment is correct only where governance mandates human release sign-off.
- ✗
Use AWS Lambda to update the endpoint whenever a new model version is created
Why it's wrong here
Model version creation fires before approval, so Lambda would deploy unapproved models, violating the approved-only requirement. The trigger must be the Model Registry approval status change, not creation. Reacting to version creation suits workflows where every registered model is automatically promoted without review.
- ✓
Create a SageMaker Pipeline that includes a model approval step and deployment step
Why this is correct
A SageMaker Pipeline can encode the approval step and subsequent deployment step as connected pipeline steps, so an approved model version automatically triggers production deployment after retraining. This automates the registry-to-endpoint promotion path the scenario requires.
- ✗
Schedule a CloudWatch Event to invoke a SageMaker update endpoint API daily
Why it's wrong here
Scheduled polling cannot react to an approval event, so deployment lags until the next run and may redeploy unchanged models. EventBridge rules triggered by Model Registry approval state changes give the immediate, event-driven deployment the scenario requires; CloudWatch scheduling suits periodic batch jobs, not approval-triggered releases.
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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.