AI Associate AI Capabilities in CRM Practice Question
A company wants to use Einstein to predict which customers are likely to churn in the next 30 days. Which type of prediction should be created in Einstein Prediction Builder?
⚠ Common exam trap
Salesforce often tests the distinction between regression (numeric prediction) and classification (categorical prediction), and candidates mistakenly choose 'Numeric prediction' or 'Regression' because churn prediction involves a time-based numeric threshold (30 days), but the output is still a binary label, not a number.
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
✓
Binary classification
Einstein Prediction Builder is designed for binary outcomes, such as whether a customer will churn (yes/no) within a specified time frame. A binary classification model predicts one of two possible labels, making it the correct choice for this churn prediction use case.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Numeric prediction
Why it's wrong here
Numeric predicts a number, not a yes/no outcome.
- ✗
Multi-class classification
Why it's wrong here
Multi-class predicts more than two categories, not suitable for churn.
- ✗
Regression
Why it's wrong here
Regression is not a supported Einstein Prediction Builder type.
- ✓
Binary classification
Why this is correct
Binary classification predicts one of two outcomes, like churn or not churn.
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