- A
Disable and re-enable Einstein Lead Scoring.
Why wrong: This does not address the missing field history tracking; the model will still lack historical data.
- B
Enable field history tracking on the Lead object and retrain the model.
Field history tracking provides the necessary historical data for scoring accuracy.
- C
Increase the lead volume to at least 50,000 records.
Why wrong: Volume is not the issue; field history tracking is.
- D
Manually update all leads with missing data to have complete records.
Why wrong: Manual updates are not scalable and don't address the lack of field history tracking.
Question 191 of 1,000
easyMultiple ChoiceObjective-mapped
Einstein Lead Scoring Confidence Drop: Enable Field History Tracking and Retrain
This AI Associate practice question tests your understanding of ai associate exam topics. 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 has been using Einstein Lead Scoring for six months. Recently, the lead score confidence has dropped from 85% to 60%. The admin reviews the model and finds that many leads have missing data in custom fields used by the model. The admin also notices that field history tracking is not enabled on the Lead object. The lead volume is adequate with over 10,000 leads. What should the admin do to improve the model's confidence?
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
✓
Enable field history tracking on the Lead object and retrain the model.
Option B is correct because field history tracking is required for Einstein Lead Scoring to capture changes over time, and retraining after enabling it will incorporate historical data. Option A is incorrect because manually updating all leads is impractical and doesn't address the root cause. Option C is incorrect because the lead volume is already adequate. Option D is incorrect because disabling and re-enabling will reset the model but not fix the missing field history.
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.
- ✗
Disable and re-enable Einstein Lead Scoring.
Why it's wrong here
This does not address the missing field history tracking; the model will still lack historical data.
- ✓
Enable field history tracking on the Lead object and retrain the model.
Why this is correct
Field history tracking provides the necessary historical data for scoring accuracy.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the lead volume to at least 50,000 records.
Why it's wrong here
Volume is not the issue; field history tracking is.
- ✗
Manually update all leads with missing data to have complete records.
Why it's wrong here
Manual updates are not scalable and don't address the lack of field history tracking.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 AI Associate exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this AI Associate question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Enable field history tracking on the Lead object and retrain the model. — Option B is correct because field history tracking is required for Einstein Lead Scoring to capture changes over time, and retraining after enabling it will incorporate historical data. Option A is incorrect because manually updating all leads is impractical and doesn't address the root cause. Option C is incorrect because the lead volume is already adequate. Option D is incorrect because disabling and re-enabling will reset the model but not fix the missing field history.
What should I do if I get this AI Associate question wrong?
Identify which AI Associate exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on AI Associate
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A sales manager wants to automatically prioritize leads based on their likelihood to convert. Which Einstein feature should the admin enable?
easy- ✓ A.Einstein Lead Scoring
- B.Einstein Activity Capture
- C.Einstein Prediction Builder
- D.Einstein Bot
Why A: Einstein Lead Scoring uses historical data to predict lead conversion probability and assign scores, enabling prioritization.
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Last reviewed: Jun 23, 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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