- A
A step to register the model in SageMaker Model Registry.
Why wrong: Model Registry is for cataloging models, not a CI/CD pipeline component for deployment.
- B
A CloudFormation template to deploy the endpoint infrastructure, enabling rollback via stack update.
Infrastructure as code allows precise rollback by redeploying a previous CloudFormation stack.
- C
A separate staging endpoint to validate the model before production deployment.
Staging allows testing in a production-like environment without impacting users.
- D
A manual approval step after staging testing.
Manual approval ensures compliance and prevents automatic deployment of untested models.
- E
A step to run SageMaker Debugger to monitor training.
Why wrong: Debugger is for training, not deployment; monitoring inference should use Model Monitor.
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 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.)
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.
Option B is correct because 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.
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.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
A manual approval step after staging testing.
Why this is correct
Manual approval ensures compliance and prevents automatic deployment of untested models.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
CloudFormation rollback works by maintaining a stack policy and change set; when a deployment fails, it reverts to the previous stack template and parameters. In a real-world scenario, if a new endpoint configuration causes a health check failure, CloudFormation can automatically trigger a rollback, restoring the previous endpoint without manual intervention. This is critical for production environments where uptime is paramount.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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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Deployment and Orchestration of ML Workflows — study guide chapter
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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: A CloudFormation template to deploy the endpoint infrastructure, enabling rollback via stack update. — Option B is correct because 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.
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: Jun 24, 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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