AI Associate AI Capabilities in CRM Practice Question
A company uses Einstein Lead Scoring and finds that leads with high scores are not converting. What should the admin do to improve prediction accuracy?
⚠ Common exam trap
Salesforce often tests the misconception that adjusting score thresholds or model parameters can fix accuracy issues, when the real solution is to refresh the training data to reflect current conversion patterns.
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 with more recent conversion data
Retraining the model with more recent conversion data allows the Einstein Lead Scoring model to adapt to changing patterns in lead behavior and conversion criteria. When high-scoring leads fail to convert, it indicates that the historical data used to train the model no longer reflects current conversion dynamics, so refreshing the training dataset improves prediction accuracy by aligning the model with recent outcomes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the scoring model's maximum score
Why it's wrong here
Scoring range does not fix data quality or model accuracy.
- ✓
Retrain the model with more recent conversion data
Why this is correct
Retraining with current data improves the model's relevance.
- ✗
Disable field-level security for scoring fields
Why it's wrong here
Field-level security should remain to protect sensitive data.
- ✗
Lower the lead conversion threshold
Why it's wrong here
Lowering threshold may increase conversions but not improve prediction.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI Associate practice question is part of Courseiva's free Salesforce certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI Associate exam.