- A
Deploy multiple variants with A/B testing
Why wrong: A/B testing routes traffic between variants, but does not directly address latency.
- B
Use Elastic Inference to attach an accelerator
Why wrong: Elastic Inference reduces cost per inference but may not resolve latency if the instance is already saturated.
- C
Switch to a ml.c5.large instance
Why wrong: Changing instance family may not address the root cause of insufficient capacity during peaks.
- D
Add an auto-scaling policy based on request count
Auto-scaling adjusts instance count to match demand, reducing latency during spikes while minimizing cost.
- E
Enable SageMaker Model Monitor
Why wrong: Model Monitor is for drift detection, not for improving latency.
Question 486 of 1,000
Reduce SageMaker Endpoint Latency with Auto Scaling
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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's SageMaker endpoint is experiencing increased latency during peak hours. The endpoint uses a single ml.m5.large instance. The deployment is critical and must maintain low latency. Which action is MOST effective to reduce latency without sacrificing cost efficiency?
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
✓
Add an auto-scaling policy based on request count
The correct answer is D because adding an auto-scaling policy based on request count directly addresses the root cause of increased latency during peak hours: insufficient compute capacity. Auto-scaling dynamically adds more ml.m5.large instances when request count rises, distributing the load and reducing latency, while scaling down during off-peak hours to maintain cost efficiency. This is the most effective solution for a critical deployment that must maintain low latency without sacrificing cost.
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.
- ✗
Deploy multiple variants with A/B testing
Why it's wrong here
A/B testing routes traffic between variants, but does not directly address latency.
- ✗
Use Elastic Inference to attach an accelerator
Why it's wrong here
Elastic Inference reduces cost per inference but may not resolve latency if the instance is already saturated.
- ✗
Switch to a ml.c5.large instance
Why it's wrong here
Changing instance family may not address the root cause of insufficient capacity during peaks.
- ✓
Add an auto-scaling policy based on request count
Why this is correct
Auto-scaling adjusts instance count to match demand, reducing latency during spikes while minimizing cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable SageMaker Model Monitor
Why it's wrong here
Model Monitor is for drift detection, not for improving latency.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Elastic Inference (Option B) as a cost-effective latency fix, but it does not address the capacity bottleneck from a single instance; the exam tests whether you recognize that scaling out is the correct approach for handling variable traffic loads.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker auto-scaling uses Amazon CloudWatch metrics (e.g., SageMakerVariantInvocationsPerInstance) to trigger scaling policies. When request count exceeds a threshold, the auto-scaling policy invokes the Application Auto Scaling API to add instances, distributing incoming requests across multiple endpoints via a load balancer. In a real-world scenario, a single ml.m5.large instance can handle roughly 100-200 requests per second (depending on model complexity), so auto-scaling to 3-4 instances during peak hours can reduce latency from seconds to milliseconds while keeping costs low during off-peak times.
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
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.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Solution Monitoring, Maintenance and Security — This question tests ML Solution Monitoring, Maintenance and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Add an auto-scaling policy based on request count — The correct answer is D because adding an auto-scaling policy based on request count directly addresses the root cause of increased latency during peak hours: insufficient compute capacity. Auto-scaling dynamically adds more ml.m5.large instances when request count rises, distributing the load and reducing latency, while scaling down during off-peak hours to maintain cost efficiency. This is the most effective solution for a critical deployment that must maintain low latency without sacrificing cost.
What should I do if I get this MLA-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
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