The answer is to switch to a larger instance, enable data compression, and reduce the number of trees in the model. This combination directly addresses the root causes of high latency and CPU utilization in SageMaker endpoints: the ml.c5.2xlarge provides more CPU cores to handle inference load, data compression reduces network I/O overhead and deserialization time, and pruning the LightGBM model lowers per-request computation. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to optimize real-time inference endpoints under strict latency constraints, often trapping candidates who overlook model-level tuning in favor of scaling alone. A common mistake is assuming auto scaling alone solves latency, but it does not reduce per-instance CPU saturation. Remember the three levers for endpoint performance: compute power, data efficiency, and model complexity—or, as a memory tip, think “Bigger box, smaller payload, lighter model.”
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 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 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 (switching to ml.c5.2xlarge) provides more CPU capacity, reducing latency. Option B (enabling SageMaker's data compression) reduces network transfer time and I/O overhead. Option D (using batch transform instead of real-time) is a fundamental change in architecture that would not meet real-time requirements. Option E (reducing the number of trees in LightGBM) directly reduces inference computation time. Option F (increasing instance count) is already handled by auto scaling, but alone may not reduce latency per request if each instance is saturated.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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
Static NAT maps one inside address to one outside address.
✓
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
Static NAT maps one inside address to one outside address.
✓
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
Static NAT maps one inside address to one outside address.
✗
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: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Modeling — This question tests Modeling — Static NAT maps one inside address to one outside address..
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 (switching to ml.c5.2xlarge) provides more CPU capacity, reducing latency. Option B (enabling SageMaker's data compression) reduces network transfer time and I/O overhead. Option D (using batch transform instead of real-time) is a fundamental change in architecture that would not meet real-time requirements. Option E (reducing the number of trees in LightGBM) directly reduces inference computation time. Option F (increasing instance count) is already handled by auto scaling, but alone may not reduce latency per request if each instance is saturated.
What should I do if I get this MLS-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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 →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.