- A
Upgrade the instance type to ml.c5.2xlarge to increase CPU resources per instance.
More CPU reduces per-request processing time, lowering latency.
- B
Reduce the number of trees in the LightGBM model to decrease inference time.
Fewer trees means faster inference, directly reducing latency.
- C
Enable SageMaker's data compression for endpoint input payloads.
Compression reduces payload size and network transfer time, improving latency.
- D
Switch to using SageMaker Batch Transform instead of a real-time endpoint.
Why wrong: Batch Transform is asynchronous and not suitable for real-time inference.
Question 1,650 of 1,755
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 machine learning team is deploying a real-time inference endpoint for a fraud detection model using Amazon SageMaker. The model is a LightGBM classifier trained on 1 GB of tabular data. The endpoint must respond within 100 ms for 99% of requests, with a throughput of 10 requests per second. During load testing, the team observes that the 99th percentile latency is 250 ms and the endpoint CPU utilization is consistently above 90%. The team has already selected an ml.c5.xlarge instance with auto scaling enabled. Which combination of actions should the team take to meet the latency requirement? (Choose 3.)
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
✓
Upgrade the instance type to ml.c5.2xlarge to increase CPU resources per instance.
Option A (upgrading to ml.c5.2xlarge) provides more CPU resources per instance, reducing CPU utilization and thus latency. Option B (reducing the number of trees in the LightGBM model) decreases the computational complexity of inference, directly lowering inference time. Option C (enabling SageMaker's data compression for endpoint input payloads) reduces the data transfer size, which can lower I/O overhead and network latency. Option D (switching to SageMaker Batch Transform) is unsuitable because it is not designed for real-time inference and would not meet the low-latency requirement. Together, options A, B, and C address the latency issue by improving compute capacity, reducing model complexity, and minimizing data transfer time.
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.
- ✓
Upgrade the instance type to ml.c5.2xlarge to increase CPU resources per instance.
Why this is correct
More CPU reduces per-request processing time, lowering latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Reduce the number of trees in the LightGBM model to decrease inference time.
Why this is correct
Fewer trees means faster inference, directly reducing latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Enable SageMaker's data compression for endpoint input payloads.
Why this is correct
Compression reduces payload size and network transfer time, improving latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch to using SageMaker Batch Transform instead of a real-time endpoint.
Why it's wrong here
Batch Transform is asynchronous and not suitable for real-time inference.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Upgrade the instance type to ml.c5.2xlarge to increase CPU resources per instance. — Option A (upgrading to ml.c5.2xlarge) provides more CPU resources per instance, reducing CPU utilization and thus latency. Option B (reducing the number of trees in the LightGBM model) decreases the computational complexity of inference, directly lowering inference time. Option C (enabling SageMaker's data compression for endpoint input payloads) reduces the data transfer size, which can lower I/O overhead and network latency. Option D (switching to SageMaker Batch Transform) is unsuitable because it is not designed for real-time inference and would not meet the low-latency requirement. Together, options A, B, and C address the latency issue by improving compute capacity, reducing model complexity, and minimizing data transfer time.
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.
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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 →
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Last reviewed: Jun 20, 2026
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