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MLA-C01 Practice Question: A company's SageMaker endpoint is experiencing…
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?
⚠ Common exam trap
A common mix-up: candidates 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.
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
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.
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.
- ✗
Enable SageMaker Model Monitor
Why it's wrong here
Model Monitor is for drift detection, not for improving latency.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.