- A
All leads are from a high-quality source
Why wrong: If all leads have similar high quality, scores might be high but not all exactly 99.
- B
The lead score field is a formula field
Why wrong: Lead score is a numeric field, not a formula field.
- C
The lead score field is not added to the page layout
Why wrong: Field visibility affects display, not score calculation.
- D
The scoring model is not yet built or activated
Einstein Lead Scoring requires a trained model; until then, scores default to 99.
Question 28 of 1,000
AI Associate Salesforce Einstein AI Features Practice Question
This AI Associate practice question tests your understanding of salesforce einstein ai features. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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.
An admin configures Einstein Lead Scoring but notices that scores for all leads are stuck at 99, even for clearly low-quality leads. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
The scoring model is not yet built or activated
Option D is correct because Einstein Lead Scoring requires the scoring model to be built and activated before it can assign scores. If the model is not yet built or activated, the system defaults to a placeholder score of 99 for all leads, regardless of their actual quality. This explains why even low-quality leads show a score of 99.
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.
- ✗
All leads are from a high-quality source
Why it's wrong here
If all leads have similar high quality, scores might be high but not all exactly 99.
- ✗
The lead score field is a formula field
Why it's wrong here
Lead score is a numeric field, not a formula field.
- ✗
The lead score field is not added to the page layout
Why it's wrong here
Field visibility affects display, not score calculation.
- ✓
The scoring model is not yet built or activated
Why this is correct
Einstein Lead Scoring requires a trained model; until then, scores default to 99.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume a uniform score of 99 indicates all leads are high-quality, when in fact it is the default placeholder value used when the scoring model is not active.
Trap categories for this question
Similar concept trap
If all leads have similar high quality, scores might be high but not all exactly 99.
Detailed technical explanation
How to think about this question
Einstein Lead Scoring uses a predictive model built on historical lead conversion data. The model must be explicitly built and activated in Setup under Einstein Lead Scoring. Until activation, the system does not run the scoring algorithm, and the lead score field remains at its default value of 99. This default is a placeholder, not a computed score, and is a common indicator that the model is not yet operational.
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.
- →
Salesforce Einstein AI Features — study guide chapter
Learn the concepts, then practise the questions
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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: The scoring model is not yet built or activated — Option D is correct because Einstein Lead Scoring requires the scoring model to be built and activated before it can assign scores. If the model is not yet built or activated, the system defaults to a placeholder score of 99 for all leads, regardless of their actual quality. This explains why even low-quality leads show a score of 99.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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