- A
An AWS Lambda function for manual approval.
Why wrong: Manual approval is not required; the condition can be automated.
- B
An AWS CodeBuild project to compile the model artifacts.
Why wrong: CodeBuild is not necessary; SageMaker handles training.
- C
The SageMaker Model Registry to approve and store the model after evaluation.
Model Registry can store model versions and track approval status.
- D
A SageMaker endpoint deployment step that runs only after approval.
Deployment step is required to create or update the endpoint.
- E
A condition step that checks if the evaluation metric exceeds the threshold.
The condition step gates the deployment based on metric values.
Question 194 of 1,000
hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: A machine learning team is building a CI/CD…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
A machine learning team is building a CI/CD pipeline to train and deploy models using Amazon SageMaker. They want to ensure that the deployment step only proceeds if the model evaluation metrics exceed a certain threshold. Which THREE components should the team include in the pipeline? (Choose 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
✓
The SageMaker Model Registry to approve and store the model after evaluation.
Option C is correct because the SageMaker Model Registry is the central component for approving and storing model versions after evaluation. It enables governance by allowing you to set approval statuses (e.g., Approved, Rejected) and track model lineage, ensuring only validated models proceed to deployment.
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.
- ✗
An AWS Lambda function for manual approval.
Why it's wrong here
Manual approval is not required; the condition can be automated.
- ✗
An AWS CodeBuild project to compile the model artifacts.
Why it's wrong here
CodeBuild is not necessary; SageMaker handles training.
- ✓
The SageMaker Model Registry to approve and store the model after evaluation.
Why this is correct
Model Registry can store model versions and track approval status.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
A SageMaker endpoint deployment step that runs only after approval.
Why this is correct
Deployment step is required to create or update the endpoint.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
A condition step that checks if the evaluation metric exceeds the threshold.
Why this is correct
The condition step gates the deployment based on metric values.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that manual approval via Lambda is required for gating deployments, but the correct approach uses SageMaker Model Registry's built-in approval mechanism combined with a condition step in the pipeline.
Detailed technical explanation
How to think about this question
Under the hood, the SageMaker Model Registry integrates with AWS CodePipeline via the SageMaker Model Building Pipeline, where a condition step evaluates metrics (e.g., accuracy, F1 score) against a threshold using SageMaker Processing or built-in evaluation jobs. If the condition passes, the model version is automatically registered with an 'Approved' status, and the deployment step (e.g., creating a SageMaker endpoint) is triggered only for approved versions, ensuring a fully automated, gated release process.
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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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: The SageMaker Model Registry to approve and store the model after evaluation. — Option C is correct because the SageMaker Model Registry is the central component for approving and storing model versions after evaluation. It enables governance by allowing you to set approval statuses (e.g., Approved, Rejected) and track model lineage, ensuring only validated models proceed to deployment.
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 30, 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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