AI0-001 AI Concepts and Foundations Practice Question
A data scientist is training a model to classify customer support tickets into categories. The dataset has 10,000 labeled examples, but the 'billing' category contains 8,000 examples while the 'technical' category contains 2,000. Which technique is most appropriate to address this imbalance before training?
⚠ Common exam trap
CompTIA often tests the misconception that adjusting thresholds post-training can compensate for class imbalance, but the trap here is that the model's internal weights are already skewed by the imbalanced training data, making threshold tuning ineffective without prior balancing.
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 random oversampling on the 'technical' category.
Random oversampling duplicates examples from the minority class ('technical') to balance the class distribution, preventing the model from becoming biased toward the majority class ('billing'). This technique directly addresses the class imbalance before training, which is critical for classification tasks where the minority class is underrepresented.
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 random oversampling on the 'technical' category.
Why this is correct
Correct; oversampling balances the classes.
- ✗
Remove all examples except 'billing' and use a one-class classifier.
Why it's wrong here
This ignores the 'technical' class entirely.
- ✗
Use accuracy as the only evaluation metric.
Why it's wrong here
Accuracy is not reliable for imbalanced data.
- ✗
Train the model as is, then adjust thresholds post-training.
Why it's wrong here
While threshold adjustment can help, addressing imbalance during preprocessing is more direct.
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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.