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AI Associate AI Capabilities in CRM Practice Question

Exhibit

{
  "predictionField": "Lead_Score__c",
  "predictionType": "BinaryClassification",
  "targetObject": "Lead",
  "recordCount": 450,
  "modelStatus": "Active"
}

Refer to the exhibit. An admin created a prediction using Einstein Prediction Builder. The prediction is configured to calculate a score on the Lead object. What does the JSON indicate about the model?

⚠ Common exam trap

Salesforce often tests the distinction between regression (numeric prediction) and classification (binary outcome) by showing a JSON with a 'probability' field, leading candidates to mistakenly think it predicts a numeric value when the 'predictedValue' field clearly indicates a categorical label.

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

It predicts a binary outcome (e.g., convert or not)

Einstein Prediction Builder for the Lead object, when configured to predict a binary outcome like 'convert or not,' outputs a JSON payload containing a 'probability' field (e.g., 0.85) and a 'predictedValue' field (e.g., 'Converted' or 'Not Converted'). The JSON shown indicates a classification model that assigns a probability to one of two discrete classes, which is the hallmark of binary classification. The presence of a 'predictedValue' field with a categorical label confirms it is not a regression (numeric) or multi-class text prediction.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • It predicts a numeric value

    Why it's wrong here

    BinaryClassification predicts a binary outcome.

  • It predicts the value of a text field

    Why it's wrong here

    Prediction field is numeric score, but the type is binary.

  • It is currently retraining the model

    Why it's wrong here

    modelStatus is Active, not Training.

  • It predicts a binary outcome (e.g., convert or not)

    Why this is correct

    Binary classification yields two possible results.

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