MLS-C01 Amazon SageMaker real-time endpoints Practice Question
A machine learning team is deploying a real-time inference endpoint for a fraud detection model using Amazon SageMaker. The model requires low latency (<100 ms) and the team expects a steady stream of requests with occasional spikes. Which instance type and deployment strategy should they use to minimize cost while meeting latency requirements?
⚠ Common exam trap
Many candidates choose auto-scaling without a minimum instance count (Option C) thinking it handles spikes cost-effectively, but they overlook the latency penalty of scaling up during a spike, which can exceed 100 ms. With a minimum instance count set to handle baseline traffic, scaling adds capacity during spikes with minimal latency impact.
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
✓
Use ml.c5 instances with a single endpoint and auto-scaling with a minimum instance count to handle baseline traffic.
Ml.c5 instances are compute-optimized for low-latency inference, and auto-scaling with a minimum instance count ensures pre-warmed capacity to handle steady traffic with spikes without cold starts, meeting the <100 ms requirement cost-effectively. This combination avoids over-provisioning while ensuring consistent performance.
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 ml.p3 instances with a multi-model endpoint.
Why it's wrong here
Incorrect. ml.p3 instances are GPU-optimized and more expensive; multi-model endpoints are useful for deploying multiple models but the cost and latency may not be optimal for a single model.
- ✗
Use AWS Lambda with a container image for serverless inference.
Why it's wrong here
Incorrect. AWS Lambda can have cold starts exceeding 100 ms, and serverless inference is not ideal for steady-state traffic with spikes; also, Lambda is not a SageMaker deployment strategy.
- ✗
Use ml.m5 instances with a production variant and auto-scaling.
Why it's wrong here
Incorrect. ml.m5 instances are general-purpose but may be less cost-effective for compute-heavy inference; auto-scaling without a minimum instance count risks scaling latency during spikes.
- ✓
Use ml.c5 instances with a single endpoint and auto-scaling with a minimum instance count to handle baseline traffic.
Why this is correct
Correct. ml.c5 instances provide compute-optimized performance at lower cost, and auto-scaling with a minimum instance count ensures pre-warmed capacity for low latency.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.