- A
Ensure that historical opportunity data includes both won and lost records
The model needs examples of both outcomes to learn effectively.
- B
Use Einstein Discovery to analyze the same data
Why wrong: Discovery analyzes but does not improve the scoring model directly.
- C
Increase the number of records by duplicating existing opportunities
Why wrong: Duplicating records does not add new information and can skew results.
- D
Select only the most relevant fields as factors in the model setup
Choosing relevant features helps the model focus on important predictors.
- E
Manually override scores for high-value opportunities
Why wrong: Manual overrides do not improve model accuracy.
Question 817 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 sales operations manager wants to improve the accuracy of Einstein Opportunity Scoring. Which TWO actions should they take? (Choose two.)
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 historical opportunity data includes both won and lost records
Option A is correct because Einstein Opportunity Scoring is a predictive model that learns from historical opportunity data to identify patterns that lead to wins or losses. Including both won and lost records ensures the model has a balanced training set, which is essential for accurately distinguishing between likely wins and losses. Without lost records, the model would be biased and unable to effectively predict negative outcomes.
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.
- ✓
Ensure that historical opportunity data includes both won and lost records
Why this is correct
The model needs examples of both outcomes to learn effectively.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Einstein Discovery to analyze the same data
Why it's wrong here
Discovery analyzes but does not improve the scoring model directly.
- ✗
Increase the number of records by duplicating existing opportunities
Why it's wrong here
Duplicating records does not add new information and can skew results.
- ✓
Select only the most relevant fields as factors in the model setup
Why this is correct
Choosing relevant features helps the model focus on important predictors.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Manually override scores for high-value opportunities
Why it's wrong here
Manual overrides do not improve model accuracy.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often think more data is always better (Option C) or that manual adjustments can improve accuracy (Option E), but Salesforce specifically tests the understanding that model accuracy depends on balanced, high-quality training data and automated feature selection.
Detailed technical explanation
How to think about this question
Einstein Opportunity Scoring uses a gradient boosting machine (GBM) algorithm that automatically selects and weights features from the opportunity object and related records (e.g., Account, Contact) to predict the likelihood of a win. The model is retrained periodically based on new data, so ensuring a representative historical dataset with both won and lost records is critical for maintaining predictive performance. In practice, a common mistake is to include only won records, which causes the model to overestimate win probabilities for all opportunities.
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: Ensure that historical opportunity data includes both won and lost records — Option A is correct because Einstein Opportunity Scoring is a predictive model that learns from historical opportunity data to identify patterns that lead to wins or losses. Including both won and lost records ensures the model has a balanced training set, which is essential for accurately distinguishing between likely wins and losses. Without lost records, the model would be biased and unable to effectively predict negative outcomes.
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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