AI0-001 AI Concepts and Foundations Practice Question
A self-driving car company is developing an object detection system using a convolutional neural network (CNN). The system needs to detect pedestrians and vehicles in real-time with high accuracy. Which technique can reduce inference time while maintaining accuracy?
⚠ Common exam trap
CompTIA often tests the misconception that adding more layers or filters always improves performance, when in fact it increases latency and resource usage, while pruning and quantization are the standard techniques for reducing inference time without sacrificing accuracy.
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
✓
Apply model pruning and quantization
Model pruning removes redundant or less important weights from the CNN, reducing computational load, while quantization converts floating-point weights to lower-precision integers (e.g., INT8). Together, they shrink model size and speed up inference without significantly degrading accuracy, making them ideal for real-time object detection in resource-constrained environments like autonomous vehicles.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Apply model pruning and quantization
Why this is correct
Pruning removes unimportant weights, and quantization reduces precision of weights, both speeding up inference while preserving accuracy.
- ✗
Use a pre-trained model and fine-tune it
Why it's wrong here
Fine-tuning a pre-trained model does not inherently reduce inference time; it may still be large.
- ✗
Add more convolutional layers
Why it's wrong here
Adding layers increases depth and computation, increasing inference time.
- ✗
Increase number of filters in each layer
Why it's wrong here
Increasing filters increases model parameters and computation, slowing down inference.
About these practice questions
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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.