Courseiva
Data for AIhardMultiple ChoiceObjective-mapped

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.

About these practice questions

One of 753 original AI Associate practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →

How Courseiva writes practice questions · Editorial policy

JA

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.