MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A machine learning team runs a SageMaker AI Pipeline that trains a model and registers it in the SageMaker AI Model Registry. A separate deployment process must promote the model to production only after a human reviewer approves the model version. The team wants to automate the promotion so that approval in the Model Registry triggers the deployment without manual intervention. Which combination of steps should the engineer implement?
⚠ Common exam trap
The trap here is trying to enforce a post-pipeline human approval inside the pipeline itself instead of reacting to the Model Registry state change event.
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
✓
Configure an Amazon EventBridge rule for the SageMaker AI Model Registry model version state change to Approved, and use it to start a deployment workflow.
Model approval in the SageMaker AI Model Registry changes the model version state and emits an event. Capturing that event with Amazon EventBridge and routing it to a deployment workflow links the human approval gate to automated promotion, which is exactly the required behavior.
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 a SageMaker AI Projects template that automatically deploys every model version as soon as it is registered.
Why it's wrong here
SageMaker AI Projects helps provision MLOps templates and CI/CD pipelines, but it does not itself gate deployment on a human approval in the Model Registry. Deploying every registered version would bypass the required review and could push unapproved models to production.
- ✗
Schedule an AWS Lambda function every minute to call DescribeModelPackage and deploy any approved model version.
Why it's wrong here
Polling DescribeModelPackage on a schedule can work but is inefficient and can deploy duplicate or unintended versions if state tracking is imperfect. It also does not react immediately to approval and adds unnecessary API calls. An event-driven approach is the intended mechanism for reacting to Model Registry approval state changes.
- ✗
Add a ConditionStep to the existing pipeline that checks the model version status and deploys the model.
Why it's wrong here
A ConditionStep evaluates during the pipeline execution, but the human approval happens after the pipeline has already registered the model version. The pipeline execution is typically complete by then, so a ConditionStep cannot observe the later approval event and would not trigger deployment at the right time.
- ✓
Configure an Amazon EventBridge rule for the SageMaker AI Model Registry model version state change to Approved, and use it to start a deployment workflow.
Why this is correct
The SageMaker AI Model Registry emits state change events when a model version moves to Approved. An EventBridge rule can match that event and invoke a target such as AWS Step Functions or AWS CodePipeline to run the deployment. This automates promotion only after the human approval step, satisfying the requirement without polling.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.