- A
Increase the score range from 1-99 to 1-100
Why wrong: Changing the score range does not address model overfitting.
- B
Retrain the model by including more recent leads and removing outdated ones
Adding more diverse, recent data can reduce overfitting and improve generalization.
- C
Manually adjust lead scores for high-scoring leads
Why wrong: Manual adjustments are not a model improvement technique and would introduce bias.
- D
Add more features to the model to capture more signals
Why wrong: Adding more features can sometimes exacerbate overfitting if they are not predictive.
Question 109 of 1,000
AI Associate Salesforce Einstein AI Features Practice Question
This AI Associate practice question tests your understanding of salesforce einstein ai features. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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Retrain the model by including more recent leads and removing outdated ones
Option B is correct because 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 By retraining the model with more recent leads and removing outdated ones, the model can adapt to current conversion behaviors and reduce overfitting, improving predictive performance.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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Retrain the model by including more recent leads and removing outdated ones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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think adding more features (Option D) always improves model accuracy, but in the context of overfitting, it often worsens the problem by increasing variance.
Detailed technical explanation
How to think about this question
Einstein Lead Scoring uses a predictive model trained on historical lead conversion data, typically employing gradient boosting or logistic regression. Overfitting occurs when the model captures noise or temporal trends (e.g., seasonal buying patterns) that no longer apply. Retraining with a sliding window of recent data (e.g., last 6 months) helps the model generalize better, as it discards stale patterns and emphasizes current conversion signals.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI Associate question test?
Salesforce Einstein AI Features — This question tests Salesforce Einstein AI Features — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Retrain the model by including more recent leads and removing outdated ones — Option B is correct because 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.
What should I do if I get this AI Associate question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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.
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