AI0-001 AI Concepts and Foundations Practice Question
A company is implementing an AI solution for fraud detection. The dataset is highly imbalanced (only 1% fraudulent transactions). Which THREE techniques are most appropriate to address class imbalance? (Select three.)
⚠ Common exam trap
CompTIA often tests the misconception that accuracy is a valid metric for imbalanced datasets, but the trap here is that candidates overlook how a high accuracy can mask poor minority class performance, leading them to select option C instead of focusing on precision-recall curves and F1 score.
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 cost-sensitive learning by assigning a higher misclassification cost to the minority class.
Cost-sensitive learning directly addresses class imbalance by assigning a higher misclassification cost to the minority class (fraudulent transactions). This forces the model to penalize false negatives more heavily, thereby improving recall for the minority class without altering the dataset distribution.
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 cost-sensitive learning by assigning a higher misclassification cost to the minority class.
Why this is correct
Cost-sensitive methods penalize minority class errors more heavily.
- ✗
Reduce the number of features using principal component analysis (PCA).
Why it's wrong here
PCA reduces dimensionality but does not address class imbalance.
- ✗
Use accuracy as the primary evaluation metric.
Why it's wrong here
Accuracy can be misleading in imbalanced datasets because a model that predicts all majority class can achieve high accuracy.
- ✓
Evaluate model performance using precision-recall curves and F1 score.
Why this is correct
Precision-recall curves focus on the minority class and are more informative than ROC for imbalanced data.
- ✓
Use synthetic oversampling (SMOTE) to create additional minority class samples.
Why this is correct
SMOTE generates synthetic examples to balance classes.
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.