AI Associate Data for AI Practice Question
A fraud detection model is being trained on transaction data where only 1% of transactions are fraudulent. The current model predicts 'non-fraud' for all transactions, achieving 99% accuracy. Which technique should be applied to improve model performance?
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
✓
Oversample the minority class or undersample the majority class
Oversampling or undersampling addresses class imbalance, allowing the model to learn minority patterns. Using more features alone doesn't fix imbalance, setting a lower threshold may help but is less common than resampling, and removing minority class is counterproductive.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the minority class to have balanced data
Why it's wrong here
Removing minority class eliminates the cases we want to predict.
- ✗
Set a lower classification threshold for fraud
Why it's wrong here
This can work but is less effective than resampling; still, best practice is to balance the dataset.
- ✗
Add more features like transaction location
Why it's wrong here
Adding features without addressing imbalance may not solve the problem.
- ✓
Oversample the minority class or undersample the majority class
Why this is correct
Resampling techniques create a more balanced training set, improving recall for fraud.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI Associate practice question is part of Courseiva's free Salesforce 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 AI Associate exam.