- A
Use a larger instance type for the endpoint
Why wrong: Larger instance may reduce latency but is not the best for spikes; auto-scaling is more cost-effective.
- B
Attach SageMaker Elastic Inference to the endpoint
Why wrong: Elastic Inference reduces per-request latency but does not handle traffic spikes.
- C
Enable SageMaker endpoint auto-scaling
Auto-scaling adds instances during spikes, reducing latency.
- D
Use SageMaker Neo to compile the model
Why wrong: Neo optimizes for edge devices, not for server-side inference.
- E
Switch to SageMaker batch transform
Why wrong: Batch transform is for offline inference, not real-time.
Question 1,002 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 has deployed a model on SageMaker for real-time inference. The endpoint is experiencing high latency during traffic spikes. Which action should the company take to reduce latency?
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
✓
Enable SageMaker endpoint auto-scaling
Option C is correct because enabling SageMaker endpoint auto-scaling allows the endpoint to dynamically adjust the number of instances based on incoming traffic, which directly reduces latency during spikes by ensuring sufficient compute capacity is available. Auto-scaling uses CloudWatch metrics (e.g., InvocationsPerInstance or latency) to trigger scale-out events, preventing queue buildup and response time degradation.
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 larger instance type for the endpoint
Why it's wrong here
Larger instance may reduce latency but is not the best for spikes; auto-scaling is more cost-effective.
- ✗
Attach SageMaker Elastic Inference to the endpoint
Why it's wrong here
Elastic Inference reduces per-request latency but does not handle traffic spikes.
- ✓
Enable SageMaker endpoint auto-scaling
Why this is correct
Auto-scaling adds instances during spikes, reducing latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use SageMaker Neo to compile the model
Why it's wrong here
Neo optimizes for edge devices, not for server-side inference.
- ✗
Switch to SageMaker batch transform
Why it's wrong here
Batch transform is for offline inference, not real-time.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that improving per-request performance (e.g., via larger instances, Elastic Inference, or Neo compilation) is the solution for handling traffic spikes, when the actual need is horizontal scaling to increase request throughput capacity.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker auto-scaling uses a target tracking scaling policy based on a predefined CloudWatch metric (e.g., SageMakerVariantInvocationsPerInstance) to maintain a target utilization level. When traffic spikes, the endpoint scales out by adding new instances, which are provisioned with the model and become available to serve requests, reducing queue depth and response times. A common subtlety is that auto-scaling has a cooldown period (default 300 seconds) to avoid thrashing, so for extremely sudden spikes, pre-warming with provisioned concurrency or predictive scaling may be needed.
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
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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Enable SageMaker endpoint auto-scaling — Option C is correct because enabling SageMaker endpoint auto-scaling allows the endpoint to dynamically adjust the number of instances based on incoming traffic, which directly reduces latency during spikes by ensuring sufficient compute capacity is available. Auto-scaling uses CloudWatch metrics (e.g., InvocationsPerInstance or latency) to trigger scale-out events, preventing queue buildup and response time degradation.
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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