AI Associate Salesforce Einstein AI Features Practice Question
A support center wants to use Einstein Case Classification to automatically assign categories to incoming cases. They have historical case data with the 'Type' field populated for 70% of cases, 'Priority' for 50%, and 'Reason' for 30%. They want to classify on 'Type' and 'Reason'. What is the best approach to maximize model accuracy?
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
✓
Start by training a model for Type only, since it has more populated records, then train a model for Reason once more data is accumulated.
Einstein Case Classification requires a minimum of 1500 records with the target field populated. Prioritizing the field with more populated records (Type) ensures a larger training set, improving accuracy. Reason can be added later once more data is available.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Einstein Prediction Builder instead, which can handle multi-class classification for both fields.
Why it's wrong here
Einstein Prediction Builder is for binary classification only, not multi-class or multi-field classification.
- ✗
Build a single model that predicts both Type and Reason simultaneously to leverage all available data.
Why it's wrong here
Einstein Case Classification builds separate models for each field; simultaneous prediction is not supported.
- ✓
Start by training a model for Type only, since it has more populated records, then train a model for Reason once more data is accumulated.
Why this is correct
Starting with Type gives a larger training set (70% of records), likely meeting the 1500 record minimum. Reason can be added later when more records have that field populated.
- ✗
Create two separate models: one for Type using all records with Type populated, and one for Reason using all records with Reason populated.
Why it's wrong here
Two models would each have limited data, especially Reason with only 30% populated, leading to poor accuracy.
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