- A
Store model artifacts in Amazon EBS volumes attached to the endpoint instances
Why wrong: Model artifacts should be stored in S3, not EBS; EBS is ephemeral.
- B
Use separate production and staging endpoints to test new models before full rollout
Testing on staging before production is a best practice.
- C
Manually track model versions using tags because SageMaker Model Registry is not available for deployment
Why wrong: SageMaker Model Registry is available and should be used for versioning.
- D
Disable CloudWatch Logs to reduce costs during inference
Why wrong: CloudWatch Logs are important for monitoring and troubleshooting; disabling is not best practice.
- E
Enable data capture on endpoints to log predictions for auditing and model monitoring
Data capture is important for model monitoring and auditing.
Quick Answer
The correct choices are enabling data capture on endpoints to log predictions for auditing and model monitoring, and using separate endpoints for different use cases to isolate traffic and avoid resource contention. This is because data capture allows you to store inference requests and responses in S3 for compliance, drift detection, and debugging, while separate endpoints prevent a high-volume production workload from impacting a low-latency real-time service. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of SageMaker deployment best practices, often appearing as a multi-select scenario where common traps include confusing EBS with S3 for model storage or disabling CloudWatch Logs, which are enabled by default. A key memory tip is to think of endpoints as dedicated lanes on a highway: each lane serves a specific traffic type, and data capture is your toll booth recording every vehicle that passes for later review.
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. 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.
Which TWO of the following are best practices for deploying machine learning models on SageMaker? (Select TWO.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
Use separate production and staging endpoints to test new models before full rollout
Option B and Option D are correct. Option A is wrong because model should be in S3, not EBS. Option C is wrong because you should use the SageMaker Model Registry for versioning. Option E is wrong because CloudWatch Logs are enabled by default, not disabled.
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.
- ✗
Store model artifacts in Amazon EBS volumes attached to the endpoint instances
Why it's wrong here
Model artifacts should be stored in S3, not EBS; EBS is ephemeral.
- ✓
Use separate production and staging endpoints to test new models before full rollout
Why this is correct
Testing on staging before production is a best practice.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Manually track model versions using tags because SageMaker Model Registry is not available for deployment
Why it's wrong here
SageMaker Model Registry is available and should be used for versioning.
- ✗
Disable CloudWatch Logs to reduce costs during inference
Why it's wrong here
CloudWatch Logs are important for monitoring and troubleshooting; disabling is not best practice.
- ✓
Enable data capture on endpoints to log predictions for auditing and model monitoring
Why this is correct
Data capture is important for model monitoring and auditing.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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: Use separate production and staging endpoints to test new models before full rollout — Option B and Option D are correct. Option A is wrong because model should be in S3, not EBS. Option C is wrong because you should use the SageMaker Model Registry for versioning. Option E is wrong because CloudWatch Logs are enabled by default, not disabled.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 22, 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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