Question 512 of 1,672
MLS-C01 Modeling Practice Question
A company uses Amazon SageMaker to host a model for real-time predictions. The model endpoint is experiencing high latency during peak hours. The data scientist wants to reduce latency without increasing cost. Which action should they take?
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 auto-scaling for the endpoint based on latency metrics
Using SageMaker's production variants with auto-scaling can help handle traffic spikes without over-provisioning, thus managing latency and cost. Switching to a larger instance would increase cost. Reducing the number of instances would increase latency. Enabling data capture adds overhead and increases 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.
- ✗
Enable data capture for the endpoint to log requests
Why it's wrong here
Data capture adds overhead and increases latency.
- ✗
Switch to a larger instance type
Why it's wrong here
Larger instances increase cost.
- ✗
Reduce the number of instances behind the endpoint
Why it's wrong here
Fewer instances would increase latency.
- ✓
Enable auto-scaling for the endpoint based on latency metrics
Why this is correct
Auto-scaling adjusts capacity to demand, maintaining low latency without over-provisioning.
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Last reviewed: Jun 20, 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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