MLS-C01 Modeling Practice Question
A company is using Amazon SageMaker to deploy a model for real-time inference. The model receives requests with varying payload sizes. The company observes occasional latency spikes. Which feature can help mitigate this?
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
✓
Amazon SageMaker Inference Recommender
SageMaker Inference Recommender provides load testing and recommendations for instance type and endpoint configuration. It can help identify optimal settings to reduce latency spikes. Multi-model endpoints are for hosting multiple models, not directly for latency spikes. Elastic Inference is for accelerating deep learning inference, not general latency. Automatic scaling adjusts capacity but not per-request latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Multi-model endpoints
Why it's wrong here
Multi-model endpoints are for hosting multiple models, not for reducing latency spikes.
- ✗
Amazon Elastic Inference
Why it's wrong here
Elastic Inference accelerates deep learning models but does not address latency spikes from varying payloads.
- ✗
Automatic scaling
Why it's wrong here
Automatic scaling adjusts capacity over time, not fine-grained per-request latency.
- ✓
Amazon SageMaker Inference Recommender
Why this is correct
Inference Recommender runs benchmarks to recommend optimal instance and endpoint configuration.
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JA
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.