AI Associate AI Fundamentals Practice Question
A nonprofit organization uses Salesforce to manage donor relationships. They have implemented Einstein Prediction Builder to predict which donors are likely to upgrade their donation level in the next 90 days. The model was built using a custom object "Donation" with fields like Amount, Frequency, and Campaign. After deployment, the predictions seem random and do not correlate with donor engagement. The admin suspects the model is not trained on enough records. The organization has 500 donors with at least two donations each. What should the admin do to improve the model?
⚠ Common exam trap
Salesforce often tests the misconception that adding more data fields or changing the prediction window can compensate for a lack of positive training records, when in fact the core requirement is a sufficient number of outcome examples for the model to learn from.
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
✓
Ensure that at least 500 records exist where the donor actually upgraded, and retrain the model.
Einstein Prediction Builder requires a minimum number of positive outcome records (upgrade events) to train a reliable model. With only 500 donors and likely far fewer upgrades, the model lacks sufficient signal. Ensuring at least 500 actual upgrade records provides the necessary positive examples for the algorithm to learn meaningful patterns, reducing randomness in predictions.
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 prediction window from 90 to 180 days to capture more upgrade events.
Why it's wrong here
This may increase events but the core issue is data volume.
- ✗
Use a different field as the prediction outcome, such as 'donation amount increase'.
Why it's wrong here
Same data volume issue persists.
- ✓
Ensure that at least 500 records exist where the donor actually upgraded, and retrain the model.
Why this is correct
Sufficient positive examples are needed.
- ✗
Add more fields to the model, such as donor age and geographic location.
Why it's wrong here
More fields without enough data won't help.
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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.