Solving Class Imbalance: Oversampling for Einstein Prediction Builder
A company uses Einstein Prediction Builder to predict customer churn. The model's accuracy is low. The admin reviews the training data and notices that only 2% of records are churned. What should the admin do to improve the model?
Quick Answer
The answer is to use oversampling techniques. When a dataset has severe class imbalance, like only 2% churned records, the model becomes biased toward predicting the majority class, resulting in low accuracy despite high apparent performance. Oversampling, such as SMOTE or random oversampling, artificially increases the minority class records in the training set, allowing Einstein Prediction Builder to learn patterns for churn more effectively. On the Salesforce AI Associate exam, this scenario tests your understanding of how class imbalance impacts predictive models and the specific remedy within Einstein Prediction Builder. A common trap is to assume adding more data overall or adjusting thresholds will fix the bias, but oversampling directly addresses the skewed distribution. Remember the memory tip: “When minority is tiny, oversampling makes it shiny.”
⚠ Common exam trap
Salesforce often tests the misconception that adding more data always improves model performance, but here the trap is that candidates overlook class imbalance and choose 'Increase the amount of training data' (Option B) without realizing that more data with the same imbalance does not solve the problem.
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 oversampling techniques.
When a dataset has severe class imbalance (only 2% churned records), the model becomes biased toward predicting the majority class (non-churned), leading to low accuracy despite high apparent performance. Oversampling techniques, such as SMOTE or random oversampling, artificially increase the number of churned records in the training set to balance the classes, allowing Einstein Prediction Builder to learn patterns for the minority class more effectively.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove the churned records.
Why it's wrong here
Removing the minority class would make the problem worse.
- ✗
Increase the amount of training data.
Why it's wrong here
More data may not fix the imbalance ratio.
- ✓
Use oversampling techniques.
Why this is correct
Oversampling balances the classes and improves model sensitivity.
- ✗
Change the prediction field.
Why it's wrong here
Changing the target does not address imbalance.
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Same concept, more angles
1 more way this is tested on AI Associate
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 fraud detection model is being trained on transaction data where only 1% of transactions are fraudulent. The current model predicts 'non-fraud' for all transactions, achieving 99% accuracy. Which technique should be applied to improve model performance?
easy- A.Remove the minority class to have balanced data
- B.Set a lower classification threshold for fraud
- C.Add more features like transaction location
- ✓ D.Oversample the minority class or undersample the majority class
Why D: Oversampling or undersampling addresses class imbalance, allowing the model to learn minority patterns. Using more features alone doesn't fix imbalance, setting a lower threshold may help but is less common than resampling, and removing minority class is counterproductive.
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.