AI0-001 AI Models and Data Engineering Practice Question
A company wants to deploy an AI model for real-time inference on edge devices with limited computational resources. Which model architecture would be MOST suitable?
⚠ Common exam trap
CompTIA often tests the misconception that any 'lightweight' or 'fast' model (like YOLOv4) is suitable for edge devices, ignoring the specific architectural optimizations (e.g., depthwise separable convolutions) that MobileNet uniquely provides for extreme resource constraints.
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
✓
MobileNet
MobileNet is specifically designed for mobile and edge devices using depthwise separable convolutions, which drastically reduce the number of parameters and computational cost while maintaining acceptable accuracy. This makes it the most suitable choice for real-time inference on resource-constrained edge hardware.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
YOLOv4
Why it's wrong here
YOLOv4 is efficient but still may be too heavy for very limited edge devices; MobileNet is more optimized.
- ✓
MobileNet
Why this is correct
MobileNet uses depthwise separable convolutions to reduce computation, ideal for edge deployment.
- ✗
ResNet-152
Why it's wrong here
ResNet-152 is a deep network with many parameters, too large for edge devices.
- ✗
BERT
Why it's wrong here
BERT is a large transformer model for NLP, not suitable for resource-constrained edge devices.
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.