- A
The prediction field contains data leakage (e.g., a future value)
Why wrong: Leakage would inflate scores in training and likely production, not cause low confidence on new cases.
- B
The admin selected too few features, causing underfitting
Why wrong: Underfitting yields low training score, not high.
- C
The training data is stale and no longer reflects current case patterns
Why wrong: Stale data would cause poor performance overall, but the training score would also be low.
- D
The model is overfitting because the number of features is too large relative to the number of training records
Overfitting leads to high training accuracy but poor generalization. Reducing features or increasing records can help.
Question 294 of 1,000
AI Associate Salesforce Einstein AI Features Practice Question
This AI Associate practice question tests your understanding of salesforce einstein ai features. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 Salesforce admin is building an Einstein Prediction Builder model to predict whether a support case will be escalated (binary: Yes/No). The dataset includes cases from the past two years. After selecting the prediction field and features, the admin notices that the model's training score is very high (0.99) but the prediction score field shows very low confidence for new cases. 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 model is overfitting because the number of features is too large relative to the number of training records
Option D is correct because a training score of 0.99 combined with low confidence on new cases is a classic symptom of overfitting. In Einstein Prediction Builder, when the model has too many features relative to the number of training records, it memorizes the training data instead of learning generalizable patterns, leading to poor performance on unseen cases.
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.
- ✗
The prediction field contains data leakage (e.g., a future value)
Why it's wrong here
Leakage would inflate scores in training and likely production, not cause low confidence on new cases.
- ✗
The admin selected too few features, causing underfitting
Why it's wrong here
Underfitting yields low training score, not high.
- ✗
The training data is stale and no longer reflects current case patterns
Why it's wrong here
Stale data would cause poor performance overall, but the training score would also be low.
- ✓
The model is overfitting because the number of features is too large relative to the number of training records
Why this is correct
Overfitting leads to high training accuracy but poor generalization. Reducing features or increasing records can help.
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
Cisco often tests the distinction between overfitting and data leakage; the trap here is that candidates may incorrectly attribute high training scores to data leakage (Option A) instead of recognizing the classic overfitting pattern of high training accuracy with low validation/prediction confidence.
Detailed technical explanation
How to think about this question
Overfitting occurs when the model captures noise rather than signal, often due to a high feature-to-record ratio. In Einstein Prediction Builder, the model uses gradient-boosted decision trees, which are prone to overfitting if not regularized or if the dataset is small. A real-world scenario is when an admin includes case-level details like 'Case Number' or 'Created Date' as features, which are unique per record and cause the model to memorize rather than generalize.
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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Salesforce Einstein AI Features — study guide chapter
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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 model is overfitting because the number of features is too large relative to the number of training records — Option D is correct because a training score of 0.99 combined with low confidence on new cases is a classic symptom of overfitting. In Einstein Prediction Builder, when the model has too many features relative to the number of training records, it memorizes the training data instead of learning generalizable patterns, leading to poor performance on unseen cases.
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.
About these practice questions
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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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