AI Associate Salesforce Einstein AI Features Practice Question
An admin wants to create a custom AI model to predict lead conversion using Einstein Prediction Builder. Which TWO items must they select when creating the model? (Choose two)
⚠ Common exam trap
It's easy for candidates to confuse the required selections (data set and prediction field) with optional or automated settings like algorithm type or feature selection, leading them to pick options that are not mandatory.
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
✓
Data set (records to train on)
The data set defines the records (e.g., leads, opportunities) that the model will use for training. Without specifying which records to train on, the model has no source of historical data to learn patterns from. Einstein Prediction Builder requires you to select a data set (such as a report or object) to provide the training examples.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Model algorithm type
Why it's wrong here
The algorithm is automatically chosen by Prediction Builder.
- ✗
Prediction explanation settings
Why it's wrong here
Explanation settings are optional and not required for model creation.
- ✓
Data set (records to train on)
Why this is correct
The dataset defines which records are used for training.
- ✗
Features (input fields)
Why it's wrong here
Features are optional; the builder automatically selects relevant fields.
- ✓
Prediction field (the field to predict)
Why this is correct
A binary outcome field like 'Converted' must be selected.
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