- A
Use a multi-model endpoint to host multiple models on the same instance.
Why wrong: Multi-model endpoints are for hosting multiple models, not for scaling a single model.
- B
Enable data capture to Amazon S3 for model monitoring and retraining.
Data capture logs predictions for monitoring and retraining, which is a best practice.
- C
Use a serverless inference endpoint to automatically scale.
Why wrong: Serverless endpoints have concurrency limits and cold starts, not suitable for 1000 TPS.
- D
Configure an auto scaling policy for the endpoint based on invocation metrics.
Auto scaling dynamically adjusts instances to handle varying load.
- E
Deploy the model on a single large instance (e.g., ml.p3.16xlarge).
Why wrong: Single instance may not handle peak load and provides no redundancy.
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 data scientist is deploying a machine learning model on Amazon SageMaker for real-time inference. The model requires low-latency predictions and must be able to handle up to 1000 requests per second. Which TWO actions should the data scientist take to ensure the endpoint can meet the performance requirements? (Choose 2.)
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
Enable data capture to Amazon S3 for model monitoring and retraining.
Option B is correct because enabling data capture to S3 allows model monitoring and retraining. Option D is correct because auto scaling adjusts instances based on load. Option A is wrong because serverless inference has a cold start and max concurrency limits unsuitable for 1000 TPS. Option C is wrong because increasing instance size alone may not be cost-effective and auto scaling is better. Option E is wrong because multi-model endpoints share resources and may cause contention.
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.
- ✗
Use a multi-model endpoint to host multiple models on the same instance.
Why it's wrong here
Multi-model endpoints are for hosting multiple models, not for scaling a single model.
- ✓
Enable data capture to Amazon S3 for model monitoring and retraining.
Why this is correct
Data capture logs predictions for monitoring and retraining, which is a best practice.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a serverless inference endpoint to automatically scale.
Why it's wrong here
Serverless endpoints have concurrency limits and cold starts, not suitable for 1000 TPS.
- ✓
Configure an auto scaling policy for the endpoint based on invocation metrics.
Why this is correct
Auto scaling dynamically adjusts instances to handle varying load.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Deploy the model on a single large instance (e.g., ml.p3.16xlarge).
Why it's wrong here
Single instance may not handle peak load and provides no redundancy.
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 MLS-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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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable data capture to Amazon S3 for model monitoring and retraining. — Option B is correct because enabling data capture to S3 allows model monitoring and retraining. Option D is correct because auto scaling adjusts instances based on load. Option A is wrong because serverless inference has a cold start and max concurrency limits unsuitable for 1000 TPS. Option C is wrong because increasing instance size alone may not be cost-effective and auto scaling is better. Option E is wrong because multi-model endpoints share resources and may cause contention.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-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.
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 20, 2026
This MLS-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 MLS-C01 exam.
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