AI0-001 AI Concepts and Foundations Practice Question
A self-driving car company is testing a perception model that detects pedestrians. The model achieves 99% accuracy on the test set but fails to detect pedestrians wearing dark clothing at night. The company wants to improve the model's robustness. Which action should the team take to best address this specific weakness?
⚠ Common exam trap
The trap here is thinking that increasing model complexity or adjusting the decision threshold can compensate for missing training data, when the core issue is a data distribution gap.
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
✓
Collect and annotate more nighttime images of pedestrians wearing dark clothing and add them to the training set.
Collecting and annotating more nighttime images of pedestrians in dark clothing is the most direct way to address the specific weakness. The model's failure indicates a gap in the training distribution, so adding representative data will help it learn the necessary visual cues. Other options either do not target the root cause or introduce trade-offs without improving fundamental recognition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Collect and annotate more nighttime images of pedestrians wearing dark clothing and add them to the training set.
Why this is correct
Adding targeted training data that represents the failure case directly addresses the model's weakness. By exposing the model to more examples of dark-clothed pedestrians at night, it can learn the relevant features and improve detection. This data-centric approach is often the most effective for specific robustness gaps.
- ✗
Increase the model's depth by adding more layers to capture more complex patterns.
Why it's wrong here
Adding layers increases model capacity but does not guarantee improved performance on underrepresented cases. Without sufficient training data for dark-clothed pedestrians at night, the model may still fail or overfit. This approach does not directly address the data gap.
- ✗
Apply data augmentation by randomly rotating and flipping existing daytime images.
Why it's wrong here
Augmenting daytime images with rotations and flips does not simulate nighttime conditions or dark clothing. The model needs examples that match the failure scenario. This augmentation may improve general invariance but will not fix the specific weakness.
- ✗
Reduce the model's confidence threshold for pedestrian detection to increase sensitivity.
Why it's wrong here
Lowering the confidence threshold may increase detection of dark-clothed pedestrians but will also increase false positives, potentially causing unnecessary braking. It does not improve the model's actual ability to recognize the features; it merely adjusts the decision boundary. This is a temporary workaround, not a robustness improvement.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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.