AI Associate Data for AI Practice Question
You are a Salesforce AI Specialist at a mid-sized manufacturing company. The company uses Einstein Lead Scoring to prioritize leads. The model was trained on historical lead data and has been in production for three months. Recently, the sales team reports that high-scoring leads are not converting as expected. You investigate and find that the model's data source includes leads from the past 18 months. However, six months ago, the company changed its lead qualification process: they started requiring a demo before scoring leads as 'qualified.' As a result, the definition of a converted lead changed. What is the best course of action to improve model performance?
⚠ Common exam trap
Many exam-takers think adjusting the threshold (Option A) is sufficient, but they fail to recognize that a change in the definition of the target variable requires retraining on a representative dataset, not just tuning a post-processing parameter.
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
✓
Retrain the model using only leads from the last six months after the process change
The change in lead qualification process six months ago introduced a data distribution shift (concept drift), making older leads no longer representative of the current conversion behavior. Retraining the model on only the last six months of data aligns the training set with the new definition of a 'converted lead,' allowing Einstein Lead Scoring to learn the updated patterns and improve prediction accuracy.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Manually adjust the model's prediction threshold to account for the new process
Why it's wrong here
Einstein Lead Scoring does not allow manual threshold adjustment; retraining is required.
- ✓
Retrain the model using only leads from the last six months after the process change
Why this is correct
This ensures the model learns from data that reflects the current conversion criteria.
- ✗
Remove the 'Demo Scheduled' field from the model to avoid bias
Why it's wrong here
The process change is not about bias; the conversion definition changed.
- ✗
Add more historical leads from before the process change to increase data volume
Why it's wrong here
Old data reflects the old conversion pattern and may confuse the model.
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