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 redundant weights and quantisation reduces numerical precision of remaining parameters, shrinking model size and memory bandwidth demands. Both cut inference latency on embedded automotive hardware while preserving detection accuracy, satisfying the real-time constraint that a full-precision CNN cannot meet.
- ✗
Use a pre-trained model and fine-tune it
Why it's wrong here
Fine-tuning a pre-trained model cuts training cost, not inference latency; the network's forward-pass computation is unchanged. It is tempting because transfer learning is the standard answer for limited labelled data, and it would be correct if the constraint were training time or dataset size rather than real-time inference.
- ✗
Add more convolutional layers
Why it's wrong here
Adding convolutional layers increases the depth of the forward pass, raising inference latency rather than reducing it. It is tempting because extra layers can improve accuracy on hard detection tasks, and depth would be the right lever if the problem were underfitting, not real-time speed.
- ✗
Increase number of filters in each layer
Why it's wrong here
More filters per layer widens each feature map, increasing multiply-accumulate operations and inference time. It is tempting because extra capacity often lifts detection accuracy, and it would suit a scenario where accuracy is short and latency is unconstrained, not the reverse.
About these practice questions
Courseiva writes every AI0-001 question from scratch — 962 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.