AI0-001 AI Security, Ethics and Governance Practice Question
A self-driving car company is testing an AI model for pedestrian detection. During simulation, the model fails to detect pedestrians in low-light conditions. The safety team wants to improve robustness without retraining the entire model from scratch. Which approach is most appropriate?
⚠ Common exam trap
CompTIA often tests the distinction between improving robustness to natural distribution shifts (e.g., low-light) via augmentation versus defending against adversarial perturbations, causing candidates to mistakenly choose adversarial training for non-adversarial scenarios.
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 data augmentation techniques to simulate low-light conditions in the training dataset.
Data augmentation techniques, such as adjusting brightness, contrast, and adding noise, can synthetically create low-light training examples from existing data. This improves the model's robustness to low-light conditions without requiring a full retraining from scratch, as it directly addresses the distribution shift in the input data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Replace the convolutional layers with transformer layers to improve attention.
Why it's wrong here
Changing architecture typically requires full retraining and may not address low-light specifically.
- ✓
Apply data augmentation techniques to simulate low-light conditions in the training dataset.
Why this is correct
Data augmentation can expand the training data to include low-light scenarios, improving robustness without full retraining.
- ✗
Use adversarial training to add imperceptible perturbations to training images.
Why it's wrong here
Adversarial training improves resistance to malicious perturbations but not necessarily to natural low-light conditions.
- ✗
Increase the model's depth by adding more convolutional layers.
Why it's wrong here
Increasing depth changes architecture and requires retraining; it may not directly address low-light performance.
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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.