AI Associate AI Fundamentals Practice Question
Exhibit
Refer to the exhibit.
```json
{
"aiModel": {
"type": "EinsteinPredictionService",
"object": "Opportunity",
"field": "Amount",
"predictionField": "WinProbability",
"trainingData": {
"records": 5000,
"features": ["Stage", "CloseDate", "Amount", "LeadSource"],
"outcomeField": "IsWon"
},
"status": "TrainingComplete",
"accuracy": 0.85
}
}
```Based on the exhibit, what does the accuracy of 0.85 indicate?
⚠ Common exam trap
Salesforce often tests the distinction between accuracy and precision, so the trap here is that candidates confuse 'accuracy' with 'precision' (the percentage of positive predictions that are correct) and incorrectly select Option B.
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
✓
85% of the model's predictions matched the actual outcomes.
Accuracy is defined as the ratio of correctly predicted instances (both true positives and true negatives) to the total number of predictions. An accuracy of 0.85 means that 85% of the model's predictions (whether 'won' or 'lost') matched the actual outcomes in the dataset. This is a standard classification metric that evaluates overall correctness, not just one class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
85% of the features are important for prediction.
Why it's wrong here
Accuracy does not measure feature importance.
- ✗
85% of predictions that the opportunity will be won are correct.
Why it's wrong here
Accuracy is overall, not per class.
- ✓
85% of the model's predictions matched the actual outcomes.
Why this is correct
Accuracy measures overall correctness.
- ✗
85% of opportunities in the training data were won.
Why it's wrong here
Accuracy is about predictions, not actual outcomes.
Go deeper
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