AI Associate AI Fundamentals Practice Question
A financial services firm wants to use AI to detect fraudulent transactions. They have a dataset with 1% fraudulent and 99% legitimate transactions. Which THREE actions should they take to address class imbalance? (Select THREE.)
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
✓
Use F1 score as the evaluation metric
Class imbalance can be addressed by resampling (oversampling minority, undersampling majority), using class weights, or using metrics like F1 that consider precision and recall.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use a larger dataset even if it is more imbalanced
Why it's wrong here
More data does not solve imbalance; the ratio remains problematic.
- ✗
Use accuracy as the primary evaluation metric
Why it's wrong here
Accuracy is misleading on imbalanced data; a model predicting 'legitimate' always would achieve 99% accuracy.
- ✓
Use F1 score as the evaluation metric
Why this is correct
F1 score balances precision and recall and is robust to imbalance.
- ✓
Assign higher class weights to the minority class during training
Why this is correct
Class weights penalize misclassifications of the minority class more heavily.
- ✓
Oversample the minority class (fraud) or undersample the majority class
Why this is correct
Resampling balances the classes, helping the model learn to detect fraud.
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