AI0-001 AI Concepts and Techniques Practice Question
A computer vision engineer is building a model to detect defects on a manufacturing line. Defects are rare, occurring in only 0.5% of images. The engineer trains a convolutional neural network and achieves 99.5% accuracy, but the model never predicts a defect. The engineer wants to address the underlying issue. Which approach is MOST appropriate?
⚠ Common exam trap
The trap here is treating high accuracy as evidence of a good model when the metric is misleading under class imbalance, leading to fixes that ignore the need to rebalance the training signal.
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 class weighting or resampling techniques to emphasize the minority defect class
The model achieves high accuracy by always predicting the majority class, a classic symptom of severe class imbalance. Class weighting or resampling techniques adjust the training process so that minority-class errors carry more weight or appear more frequently, compelling the model to learn defect patterns. The other options do not target the imbalance and are unlikely to resolve the failure to detect rare defects.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Switch from accuracy to mean squared error as the evaluation metric
Why it's wrong here
Mean squared error is a regression loss and is not appropriate for classification. Even if used, it would not resolve the imbalance and could further dilute the signal from rare defects. The correct action is to change the training objective or data distribution, not to replace accuracy with a regression metric that provides no meaningful classification evaluation.
- ✗
Increase the learning rate to help the model escape the majority-class solution
Why it's wrong here
Increasing the learning rate may destabilize training but does not directly address the severe class imbalance. The model's tendency to predict only the majority class stems from the loss function being dominated by non-defect examples, not from optimization speed. A higher learning rate could cause divergence or overshooting, and it does not rebalance the influence of the rare defect class.
- ✓
Apply class weighting or resampling techniques to emphasize the minority defect class
Why this is correct
Class weighting or resampling directly counteracts the imbalance by increasing the cost of misclassifying defects or by presenting more defect examples during training. This forces the model to learn defect features instead of defaulting to the majority class. It is the standard, targeted remedy for a model that achieves high accuracy but fails on the rare class.
- ✗
Add more convolutional layers to increase model capacity
Why it's wrong here
Adding layers increases capacity but does not address the fact that the loss is dominated by the majority class. A more complex model may still ignore defects because the gradient signal from the rare class is too weak. The root cause is the imbalance, not insufficient model complexity, so architectural changes alone are unlikely to fix the failure to predict defects.
Visual reference
About these practice questions
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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.