The answer is to use SageMaker Neo to compile the model for the target instance. SageMaker Neo optimizes machine learning models for a specific hardware platform by applying runtime and kernel optimizations, which can dramatically reduce inference latency by enabling the model to better leverage the GPU’s parallel processing capabilities. In this scenario, the high CPU utilization paired with low GPU utilization indicates the model is not efficiently using the GPU, and Neo’s compilation directly addresses this bottleneck without requiring a more expensive instance. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker Neo as a model optimization service, often appearing as a distractor against options like increasing instance count (which improves throughput, not per-request latency) or switching to a CPU instance (which would worsen GPU underutilization). A key memory tip: when you see high CPU and low GPU with stable request volume, think “Neo compilation to unlock the GPU.”
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 runs a real-time fraud detection model on a SageMaker endpoint. The model is a TensorFlow neural network trained on transactional data. The endpoint uses a single ml.p3.2xlarge instance. Recently, the application’s latency has increased from 50ms to 500ms on average. The CloudWatch metrics show that CPU utilization is at 90%, GPU utilization is at 30%, and memory utilization is at 40%. The number of requests per second has remained stable. The ML team suspects the model is not fully utilizing the GPU. What action should the team take to reduce latency without changing the instance type?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use SageMaker Neo to compile the model for the target instance
Optimizing the model for inference using SageMaker Neo can reduce latency by better leveraging GPU. Option A is wrong because increasing instances only helps throughput, not per-request latency. Option B is wrong because SageMaker Batch Transform is for offline inference. Option D is wrong because CPU instance would not improve GPU utilization.
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.
✗
Switch to SageMaker Batch Transform to process requests in batches
Why it's wrong here
Not for real-time inference.
✗
Change the endpoint to a compute-optimized instance like ml.c5.large
Why it's wrong here
Would reduce GPU utilization further.
✓
Use SageMaker Neo to compile the model for the target instance
Why this is correct
Neo optimizes model to better utilize GPU.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of instances behind the endpoint and use a load balancer
Why it's wrong here
Increases throughput but not per-request latency.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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.
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 SageMaker Neo to compile the model for the target instance — Optimizing the model for inference using SageMaker Neo can reduce latency by better leveraging GPU. Option A is wrong because increasing instances only helps throughput, not per-request latency. Option B is wrong because SageMaker Batch Transform is for offline inference. Option D is wrong because CPU instance would not improve GPU utilization.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is deploying a machine learning model for real-time fraud detection using Amazon SageMaker. The model must have a p99 inference latency under 50ms. Which TWO actions should the ML team take to meet the latency requirement?
medium
A.Use a multi-model endpoint to reduce cold starts.
✓ B.Use SageMaker Neo to compile and optimize the model for the target instance type.
C.Use SageMaker Batch Transform for near-real-time inference.
D.Configure automatic scaling to add instances based on CPU utilization.
✓ E.Select a GPU instance type such as ml.g4dn.xlarge.
Why B: Using SageMaker Neo (A) optimizes the model for the target hardware, reducing latency. Using GPU instances (B) can accelerate inference for compute-intensive models. SageMaker Batch Transform (C) is for offline inference. Automatic scaling (D) handles throughput, not latency. Multi-model endpoint (E) can help with many models but not single-model latency.
Last reviewed: Jun 20, 2026
Question Discussion
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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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