AI0-001 AI Implementation and Operations Practice Question
A company deployed a machine learning model on a cloud inference service. Users report high latency during peak hours. The model is deployed on a single instance. Which action should the team take to reduce latency without significant architectural changes?
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 autoscaling for the inference instances
Enabling autoscaling allows the inference service to automatically add instances during high demand, distributing the load and reducing latency. Increasing the model size would worsen latency. Switching to a batch inference pipeline would increase latency for real-time requests. Adding an API gateway does not address compute capacity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the model size to improve accuracy
Why it's wrong here
A larger model requires more computation, increasing latency.
- ✗
Switch to a batch inference pipeline
Why it's wrong here
Batch inference increases latency for real-time requests.
- ✓
Enable autoscaling for the inference instances
Why this is correct
Autoscaling adds capacity during peak demand, reducing latency.
- ✗
Add an API gateway to route requests
Why it's wrong here
An API gateway adds routing overhead and does not increase compute capacity.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.