AI Associate Salesforce Einstein AI Features Practice Question
A company uses Einstein Lead Scoring and notices that leads with a score above 90 are not converting as expected. They suspect the model is overfit to historical patterns. What should they do to improve model performance?
⚠ Common exam trap
Many exam-takers think adding more features (Option D) always improves model accuracy, but in the context of overfitting, it often worsens the problem by increasing variance.
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Retrain the model by including more recent leads and removing outdated ones
Overfitting occurs when a model learns historical patterns that are no longer relevant. By retraining the model with more recent leads and removing outdated ones, the model can adapt to current conversion behaviors and reduce overfitting, improving predictive performance.
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 score range from 1-99 to 1-100
Why it's wrong here
Changing the score range does not address model overfitting.
- ✓
Retrain the model by including more recent leads and removing outdated ones
Why this is correct
Adding more diverse, recent data can reduce overfitting and improve generalization.
- ✗
Manually adjust lead scores for high-scoring leads
Why it's wrong here
Manual adjustments are not a model improvement technique and would introduce bias.
- ✗
Add more features to the model to capture more signals
Why it's wrong here
Adding more features can sometimes exacerbate overfitting if they are not predictive.
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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.