AI Associate AI Capabilities in CRM Practice Question
Exhibit
{
"predictionDefinition": {
"type": "Einstein Prediction Builder",
"targetObject": "Case",
"predictedField": "Status",
"outcome": "Closed within 24 hours",
"trainingRecords": 500,
"fieldsUsed": ["Subject", "Description", "Priority", "Origin", "Type"],
"modelAccuracy": 72%,
"lastTrainingDate": "2024-01-15"
}
}Refer to the exhibit. An admin built a prediction model for case closure within 24 hours. The model accuracy is 72% with 500 training records. Which change would most likely improve accuracy?
⚠ Common exam trap
Salesforce often tests the misconception that adding more fields always improves accuracy, when in reality, irrelevant or noisy features can degrade performance, while increasing sample size is a more reliable method to boost 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
✓
Increase the training sample size to 5000 records
Increasing the training sample size from 500 to 5000 records provides the model with more data to learn patterns from, which reduces overfitting and improves generalization. In CRM AI models, larger datasets typically lead to higher accuracy because the algorithm can better capture underlying relationships without being skewed by noise in a small sample.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the outcome to 'Escalated'
Why it's wrong here
Changes the prediction goal, not accuracy of current.
- ✓
Increase the training sample size to 5000 records
Why this is correct
More data typically improves accuracy.
- ✗
Remove the 'Subject' field from the model
Why it's wrong here
Removing a field may reduce predictive power.
- ✗
Add more fields like 'Comments'
Why it's wrong here
Could introduce noise without enough data.
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