AI0-001 AI Models and Data Engineering Practice Question
An AI model is deployed to a mobile app with limited computational resources. The model is a deep neural network with high latency. Which technique is best to reduce inference time?
⚠ Common exam trap
The AI0-001 exam often tests the misconception that increasing batch size or model size improves performance on edge devices, when in fact these techniques increase resource demands and latency in low-resource environments.
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
✓
Quantization
Quantization reduces the precision of the model's weights and activations (e.g., from 32-bit floating point to 8-bit integer), which decreases memory footprint and speeds up computation on resource-constrained devices like mobile phones. This directly lowers inference latency without requiring additional hardware or architectural changes.
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 batch size
Why it's wrong here
Batch size affects throughput but not the latency of a single inference request.
- ✗
Add more layers
Why it's wrong here
Adding layers deepens the network and increases inference time.
- ✗
Use a larger model
Why it's wrong here
A larger model would increase computational requirements and latency.
- ✓
Quantization
Why this is correct
Quantization reduces model size and speeds up inference by using lower-precision arithmetic.
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.