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MLA-C01 Practice Question: An MLOps engineer is designing a CI/CD pipeline…
An MLOps engineer is designing a CI/CD pipeline for deploying machine learning models to a production SageMaker endpoint. The pipeline should include automated testing, approval gates, and rollback capability. Which THREE components should be included in the pipeline? (Select THREE.)
⚠ Common exam trap
Candidates often confuse model registry steps (Option A) or training monitoring tools (Option E) with deployment pipeline components, but the question specifically asks for components that enable automated testing, approval gates, and rollback capability in the CI/CD pipeline for deploying to a production SageMaker endpoint.
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
✓
A CloudFormation template to deploy the endpoint infrastructure, enabling rollback via stack update.
Using a CloudFormation template to deploy the SageMaker endpoint infrastructure enables rollback via stack update. If a deployment fails, CloudFormation can automatically roll back the stack to the previous known good state, ensuring infrastructure consistency and reducing downtime.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A step to register the model in SageMaker Model Registry.
Why it's wrong here
Model Registry is for cataloging models, not a CI/CD pipeline component for deployment.
- ✓
A CloudFormation template to deploy the endpoint infrastructure, enabling rollback via stack update.
Why this is correct
Infrastructure as code allows precise rollback by redeploying a previous CloudFormation stack.
- ✓
A separate staging endpoint to validate the model before production deployment.
Why this is correct
Staging allows testing in a production-like environment without impacting users.
- ✓
A manual approval step after staging testing.
Why this is correct
Manual approval ensures compliance and prevents automatic deployment of untested models.
- ✗
A step to run SageMaker Debugger to monitor training.
Why it's wrong here
Debugger is for training, not deployment; monitoring inference should use Model Monitor.
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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.