AI Associate Data for AI Practice Question
A financial services company uses Salesforce AI to detect fraudulent transactions. The dataset has 1 million legitimate transactions and only 1,000 fraudulent ones. The model trained with default parameters achieves 99.9% accuracy but identifies no fraud (precision and recall of 0). The data scientist wants to maximize fraud detection (recall) while minimizing false positives. Which approach is most effective?
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 SMOTE to generate synthetic fraud samples to balance the dataset.
With extreme imbalance, oversampling the minority class (e.g., SMOTE) generates synthetic fraud examples, helping the model learn fraud patterns and improve recall without discarding legitimate data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the weight of the majority class in the loss function.
Why it's wrong here
Weighting the majority class would worsen recall; the model already overweights the majority.
- ✓
Use SMOTE to generate synthetic fraud samples to balance the dataset.
Why this is correct
SMOTE creates synthetic instances of the minority class, allowing the model to learn fraud patterns effectively and improve recall.
- ✗
Train multiple models on different random subsets and average predictions.
Why it's wrong here
Bagging may not address imbalance; the ensemble will still be biased toward the majority.
- ✗
Use a simpler model to avoid overfitting on the majority class.
Why it's wrong here
Simplicity does not fix imbalance; the model may still ignore the minority class.
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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.