AI0-001 AI Concepts and Foundations Practice Question
A manufacturing company uses a computer vision AI to inspect products on an assembly line for defects. The AI model was trained on images from a single camera angle under bright, uniform lighting. Recently, the company moved the inspection station to a different part of the factory where lighting is dimmer and varies due to nearby windows. The model now misclassifies many non-defective products as defective, causing false alarms and production delays. The team has limited labeled data from the new environment. Which action should the team take to restore inspection accuracy while minimizing downtime?
⚠ Common exam trap
CompTIA often tests the misconception that simply adjusting a threshold or reverting to old conditions is a valid fix, when the correct approach is to adapt the model to the new data distribution using domain adaptation.
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 domain adaptation techniques using a small set of labeled images from the new environment
Domain adaptation techniques allow a model trained on a source domain (bright, uniform lighting) to generalize to a target domain (dim, variable lighting) using only a small set of labeled images from the new environment. This approach minimizes downtime because it avoids the need for large-scale data collection or retraining from scratch, and it directly addresses the distribution shift that causes false positives.
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 domain adaptation techniques using a small set of labeled images from the new environment
Why this is correct
Domain adaptation adjusts the model to new conditions with minimal data.
- ✗
Increase the defect classification threshold to reduce false positives
Why it's wrong here
Does not address the root cause and may miss real defects.
- ✗
Revert to the previous lighting setup by reinstalling bright, uniform lights
Why it's wrong here
This is expensive and may not be possible in the new location.
- ✗
Retrain the model from scratch using a large dataset of images from the new environment
Why it's wrong here
Requires extensive labeling and downtime.
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.