Quick Answer
The answer is to use the Synthetic Minority Over-sampling Technique (SMOTE) for handling class imbalance with SMOTE before training. SMOTE works by creating synthetic samples for the minority churn class through interpolation between existing minority instances, rather than simply duplicating data, which prevents the model from becoming biased toward the 95% non-churn majority and improves recall for the critical churn class. On the Salesforce AI Associate exam, this scenario tests your understanding of imbalanced data preprocessing, often appearing in questions about predictive modeling where the target distribution is skewed; a common trap is choosing random undersampling of the majority class, which discards valuable data, or simple oversampling, which leads to overfitting. Remember the memory tip: SMOTE “synthesizes” new minority points, so think “SMOTE creates, don’t delete or repeat.”
This AI Associate practice question tests your understanding of data for ai. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Apply Principal Component Analysis (PCA) for dimensionality reduction
Why wrong: PCA does not handle class imbalance.
Create interaction features between existing variables
Why wrong: Interaction features do not fix imbalance.
Use accuracy as the evaluation metric
Why wrong: Accuracy is misleading for imbalanced data.
Use Synthetic Minority Over-sampling Technique (SMOTE)
SMOTE creates synthetic examples of the minority class.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
SMOTE works by selecting a minority class sample, finding its k-nearest neighbors (typically k=5), and creating a synthetic sample along the line segment connecting the sample to a randomly chosen neighbor. This avoids the overfitting risk of random oversampling (which duplicates existing samples) and the information loss of undersampling. In real-world churn prediction, SMOTE is often combined with ensemble methods like Random Forest or XGBoost to further improve robustness against imbalance.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A telecom company uses Einstein Discovery to predict customer churn. The training dataset contains 100,000 records, but only 5% represent churned customers. The model achieves 95% accuracy on a holdout test set, but the recall for churn is only 20%. The business wants to proactively retain at-risk customers, so they need to identify as many churners as possible. What action should the data scientist take to improve churn recall?
mediumWhy C: Class imbalance causes the model to favor the majority class. Oversampling the minority class (e.g., using SMOTE) balances the dataset, helping the model learn churn patterns better and improve recall.
Last reviewed: Jun 30, 2026
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