AI Associate Salesforce Einstein AI Features Practice Question
A company wants to build a custom AI prediction model that predicts whether a customer will churn (yes/no) based on Salesforce data. They have historical data on churned and retained customers. Which Einstein feature should they use, and what type of prediction field is required?
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
✓
Einstein Prediction Builder, with a picklist prediction field containing 'Churned' and 'Not Churned'
Einstein Prediction Builder allows custom binary predictions. The prediction field must be a picklist with two values (e.g., 'Yes' and 'No') representing the outcome.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Einstein Lead Scoring, with a formula field
Why it's wrong here
Lead Scoring scores leads 1-99; it is not a custom prediction model.
- ✓
Einstein Prediction Builder, with a picklist prediction field containing 'Churned' and 'Not Churned'
Why this is correct
Prediction Builder uses binary classification supported by a picklist field with exactly two values.
- ✗
Einstein Discovery, with a numeric prediction field
Why it's wrong here
Discovery provides analysis, not a custom prediction model; a binary prediction requires a picklist field.
- ✗
Einstein Prediction Builder, with a checkbox prediction field
Why it's wrong here
Prediction Builder does not support checkboxes for the prediction field; it requires a picklist with two values.
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