- A
Use a smaller instance type
A smaller instance can reduce cost while meeting performance.
- B
Set up a scaling policy to scale down to zero
Why wrong: Scaling to zero is not possible for real-time endpoints.
- C
Switch to a multi-model endpoint
Why wrong: This adds complexity and may not reduce cost if only one model.
- D
Use a batch transform job instead
Why wrong: Batch transform is not real-time.
- E
Move to a serverless inference endpoint
Why wrong: Serverless may introduce cold start latency.
Question 330 of 1,755
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 company is running a real-time inference endpoint on Amazon SageMaker. The endpoint is using an ml.c5.xlarge instance. Over the past month, the CPU utilization has been consistently below 10%, and the latency is well within requirements. The company wants to reduce costs. What should they do?
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 a smaller instance type
The correct answer is A because the CPU utilization is consistently below 10%, indicating significant over-provisioning. Downgrading to a smaller instance type (e.g., ml.c5.large or ml.t3.medium) directly reduces the per-hour cost while still meeting the latency requirements. This is the most straightforward cost optimization when the current instance is underutilized and performance is already satisfactory.
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 smaller instance type
Why this is correct
A smaller instance can reduce cost while meeting performance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set up a scaling policy to scale down to zero
Why it's wrong here
Scaling to zero is not possible for real-time endpoints.
- ✗
Switch to a multi-model endpoint
Why it's wrong here
This adds complexity and may not reduce cost if only one model.
- ✗
Use a batch transform job instead
Why it's wrong here
Batch transform is not real-time.
- ✗
Move to a serverless inference endpoint
Why it's wrong here
Serverless may introduce cold start latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may overcomplicate the solution by considering advanced AWS features like multi-model endpoints or serverless inference, when the simplest and most effective fix is to right-size the instance based on the observed utilization metrics.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker endpoints use EC2 instances billed per second, so downsizing from ml.c5.xlarge (4 vCPU, 8 GiB) to ml.c5.large (2 vCPU, 4 GiB) halves the compute cost. The CPU utilization of <10% on the xlarge suggests the model is lightweight or the request rate is low, so a smaller instance will still have headroom. In a real-world scenario, if the model uses GPU for inference, downsizing CPU instances would not help, but here the instance is CPU-based, making instance size reduction the most direct cost-saving measure.
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 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.
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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: Use a smaller instance type — The correct answer is A because the CPU utilization is consistently below 10%, indicating significant over-provisioning. Downgrading to a smaller instance type (e.g., ml.c5.large or ml.t3.medium) directly reduces the per-hour cost while still meeting the latency requirements. This is the most straightforward cost optimization when the current instance is underutilized and performance is already satisfactory.
What should I do if I get this MLS-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: Jul 4, 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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