AI0-001 AI Infrastructure and Technologies Practice Question
During inference, a model served via a REST API occasionally returns high latency due to cold starts. The team uses a containerized service on Kubernetes with horizontal pod autoscaling. Which solution minimizes cold start impact while controlling cost?
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 a sidecar container that pre-warms the model and set a minimum replica count
A sidecar warm-up agent and a minimum replica count keep pods ready. Increasing resources may not fix cold starts; autoscaling based on request count may lag; vertical scaling helps but not directly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure the autoscaler based on request count with a shorter cooldown period
Why it's wrong here
Shorter cooldown may cause thrashing and still has startup delay.
- ✗
Increase CPU and memory requests for the inference container
Why it's wrong here
More resources do not eliminate cold starts; they may reduce latency after startup but not the initial delay.
- ✗
Switch to vertical pod autoscaling
Why it's wrong here
Vertical scaling adjusts resource allocation but does not address cold starts.
- ✓
Use a sidecar container that pre-warms the model and set a minimum replica count
Why this is correct
Pre-warming ensures the model is loaded; minimum replicas keep pods ready, reducing cold starts.
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.