- A
Retrain the Einstein Lead Scoring model with the latest lead data.
Retraining with recent data improves model accuracy.
- B
Manually override the lead scores for a sample of leads.
Why wrong: Manual overrides are not a best practice for improving model accuracy.
- C
Increase the field history retention period for lead fields.
Why wrong: Field history retention does not retrain the model.
- D
Adjust field-level security to allow the model to access more fields.
Why wrong: Field-level security does not affect model training.
Question 899 of 1,000
mediumMultiple ChoiceObjective-mapped
AI Associate Practice Question: A sales team is using Einstein Lead Scoring, but…
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 sales team is using Einstein Lead Scoring, but the scores for new leads seem inconsistent and not reflecting recent conversion patterns. The admin checks the model and finds it was trained three months ago. Which action should the admin take to improve model accuracy?
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 Einstein Lead Scoring model with the latest lead data.
Option A is correct because retraining the Einstein Lead Scoring model with the most recent lead data will incorporate current conversion patterns, improving accuracy. Option B is incorrect because manually overriding scores undermines the model's machine learning basis and is not scalable. Option C is incorrect because increasing field history retention affects historical tracking, not model retraining or score accuracy. Option D is incorrect because field-level security controls data access for users, not model training inputs.
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.
- ✓
Retrain the Einstein Lead Scoring model with the latest lead data.
Why this is correct
Retraining with recent data improves model accuracy.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Manually override the lead scores for a sample of leads.
Why it's wrong here
Manual overrides are not a best practice for improving model accuracy.
- ✗
Increase the field history retention period for lead fields.
Why it's wrong here
Field history retention does not retrain the model.
- ✗
Adjust field-level security to allow the model to access more fields.
Why it's wrong here
Field-level security does not affect model training.
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: Retrain the Einstein Lead Scoring model with the latest lead data. — Option A is correct because retraining the Einstein Lead Scoring model with the most recent lead data will incorporate current conversion patterns, improving accuracy. Option B is incorrect because manually overriding scores undermines the model's machine learning basis and is not scalable. Option C is incorrect because increasing field history retention affects historical tracking, not model retraining or score accuracy. Option D is incorrect because field-level security controls data access for users, not model training inputs.
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 →
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Last reviewed: Jun 22, 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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