AI0-001 Implementing AI Solutions Practice Question
A data scientist is preparing a dataset for a binary classification model to detect fraudulent transactions. The dataset contains 1% fraudulent and 99% legitimate transactions. The goal is to maximize recall for the fraud class while maintaining a precision above 0.5. Which data preparation strategy 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
✓
Apply SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic fraud examples
Handling imbalanced data typically requires resampling. For recall maximization with moderate precision constraint, oversampling the minority class (e.g., SMOTE) is effective. Undersampling loses too many majority samples, and using raw data leads the model to predict the majority class.
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 undersampling of the majority class until the dataset is balanced
Why it's wrong here
Undersampling discards many legitimate transactions, losing valuable information and reducing model generalization.
- ✗
Remove all duplicate transactions from the dataset
Why it's wrong here
Deduplication does not address class imbalance; fraud examples are rare and duplicates may be legitimate.
- ✗
Use the raw dataset without any resampling, relying on class weights during training
Why it's wrong here
Class weights help but raw imbalance can still cause the model to favor the majority class, harming recall.
- ✓
Apply SMOTE (Synthetic Minority Oversampling Technique) to generate synthetic fraud examples
Why this is correct
SMOTE creates synthetic minority samples, balancing the classes without losing majority data, improving recall while maintaining reasonable precision.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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