AI Associate Data for AI Practice Question
A data scientist is building a predictive model for customer churn using Salesforce data. The dataset has 20 features, and the target variable is highly imbalanced (5% churn, 95% non-churn). Which technique should be applied to handle the class imbalance before training?
⚠ Common exam trap
Salesforce often tests the misconception that any data preprocessing technique (like PCA or feature engineering) can fix class imbalance, when in fact only resampling methods (SMOTE, ADASYN) or cost-sensitive learning directly address the skewed target distribution.
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 Synthetic Minority Over-sampling Technique (SMOTE)
SMOTE (Synthetic Minority Over-sampling Technique) is the correct choice because it generates synthetic samples for the minority class (churn) by interpolating between existing minority instances, effectively balancing the dataset without simply duplicating data. This prevents the model from being biased toward the majority class (non-churn) and improves recall for the churn class, which is critical in imbalanced classification problems.
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 Principal Component Analysis (PCA) for dimensionality reduction
Why it's wrong here
PCA does not handle class imbalance.
- ✗
Create interaction features between existing variables
Why it's wrong here
Interaction features do not fix imbalance.
- ✗
Use accuracy as the evaluation metric
Why it's wrong here
Accuracy is misleading for imbalanced data.
- ✓
Use Synthetic Minority Over-sampling Technique (SMOTE)
Why this is correct
SMOTE creates synthetic examples of the minority class.
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