The answer is to change the instance type to a GPU instance such as ml.g4dn.xlarge. This is the most cost-effective fix because the model is heavily CPU-bound, meaning the CPU is the bottleneck for per-request latency; offloading computation to a GPU accelerates the parallel processing of inference operations, directly reducing p99 latency below the 100ms threshold. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding that scaling horizontally (adding more instances) improves throughput but does not reduce per-request latency when the bottleneck is compute-bound, not I/O-bound. A common trap is choosing automatic scaling or multi-model endpoints, which address load variance or model multiplicity, not single-model CPU saturation. Memory tip: “CPU-bound? Go GPU—more instances just queue the same bottleneck.”
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 deploys a real-time inference endpoint using Amazon SageMaker with an ML model that has strict latency requirements. The endpoint currently uses a single ml.c5.xlarge instance. During a load test, the p99 latency exceeds the 100ms threshold. The team adds more instances but latency does not improve because the model is heavily CPU-bound. What is the MOST cost-effective change to meet the latency requirement?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Change the instance type to a GPU instance such as ml.g4dn.xlarge.
Switching to an instance with GPU acceleration (e.g., ml.g4dn.xlarge) offloads computation to GPU, reducing CPU-bound latency. More instances (B) increase throughput but not per-request latency if the model is CPU-bound. Multi-model endpoints (C) help with many models but not single-model latency. Automatic scaling (D) helps with varying load but not per-request latency improvement.
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.
✓
Change the instance type to a GPU instance such as ml.g4dn.xlarge.
Why this is correct
GPU instances accelerate model inference, reducing per-request latency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a multi-model endpoint to serve multiple models on the same instance.
Why it's wrong here
Multi-model endpoints improve resource utilization but not per-model latency.
✗
Enable automatic scaling based on inference latency.
Why it's wrong here
Scaling adjusts capacity but does not reduce per-request latency.
✗
Increase the number of instances and use a target tracking scaling policy.
Why it's wrong here
Adding instances spreads load but does not reduce per-request latency for CPU-bound models.
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.
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: Change the instance type to a GPU instance such as ml.g4dn.xlarge. — Switching to an instance with GPU acceleration (e.g., ml.g4dn.xlarge) offloads computation to GPU, reducing CPU-bound latency. More instances (B) increase throughput but not per-request latency if the model is CPU-bound. Multi-model endpoints (C) help with many models but not single-model latency. Automatic scaling (D) helps with varying load but not per-request latency improvement.
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 using Amazon SageMaker to host a real-time inference endpoint for a natural language processing model. The endpoint is configured with an ml.m5.large instance. After deployment, the company observes that the inference latency is higher than expected, and the endpoint is experiencing CPU utilization near 100% during peak hours. The model is a PyTorch model that uses a transformer architecture. The company wants to reduce latency without increasing cost significantly. Which approach should the company take?
medium
A.Configure the endpoint with Auto Scaling to add more instances during peak hours.
B.Switch to batch transform for inference.
C.Attach an Elastic Inference accelerator to the existing instance.
✓ D.Change the endpoint instance type to ml.g4dn.xlarge to use GPU acceleration.
Why D: The issue is high CPU utilization causing latency. Using a GPU instance (ml.g4dn.xlarge) can accelerate inference for transformer models due to parallel processing, reducing latency. Option C is correct. Option A (Elastic Inference) may help but is less effective than a full GPU for transformer models; also, it adds complexity. Option B (Auto Scaling) helps with traffic but does not reduce per-request latency. Option D (batch transform) is for offline inference, not real-time.
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.
Question Discussion
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