- A
Ensure the 'Escalated__c' field has both 'True' and 'False' values in the training data.
Binary classification requires both outcomes present; otherwise the model cannot learn the difference.
- B
Set the prediction window to the next 30 days.
Why wrong: Prediction window is for forecasting future events, not for classifying past data.
- C
Select at least 20 features from the case object.
Why wrong: There is no minimum feature count; feature selection should be relevant, not numerous.
- D
Ensure the data set contains at least 500 records.
Why wrong: While more data helps, the immediate requirement is both classes present. Even with many records, if all are escalated, the model fails.
Question 428 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.
An admin is training a new Einstein Prediction Builder model to predict whether a support case will be escalated (binary). They have selected the prediction field 'Escalated__c' and the data set of all cases from the past year. Which step is essential to ensure the model can distinguish between escalated and non-escalated cases?
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 the 'Escalated__c' field has both 'True' and 'False' values in the training data.
Option A is correct because Einstein Prediction Builder requires the target prediction field to contain both positive and negative examples (e.g., 'True' and 'False') in the training data. Without both values, the model cannot learn the decision boundary between escalated and non-escalated cases, making binary classification impossible.
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 the 'Escalated__c' field has both 'True' and 'False' values in the training data.
Why this is correct
Binary classification requires both outcomes present; otherwise the model cannot learn the difference.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Set the prediction window to the next 30 days.
Why it's wrong here
Prediction window is for forecasting future events, not for classifying past data.
- ✗
Select at least 20 features from the case object.
Why it's wrong here
There is no minimum feature count; feature selection should be relevant, not numerous.
- ✗
Ensure the data set contains at least 500 records.
Why it's wrong here
While more data helps, the immediate requirement is both classes present. Even with many records, if all are escalated, the model fails.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that more features or larger datasets are always better, but the essential requirement for binary classification is that the target field has both outcome values present in the training data.
Detailed technical explanation
How to think about this question
Under the hood, Einstein Prediction Builder uses a gradient-boosted decision tree algorithm that requires both classes to be present in the training set to calculate class weights and split criteria. A real-world scenario: if only escalated cases exist in the training data, the model will always predict 'escalated', achieving 100% accuracy on training but failing completely on new data containing non-escalated cases.
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
- →
Salesforce Einstein AI Features practice questions
Targeted practice on this topic area only
- →
All AI Associate questions
1,000 questions across all exam domains
- →
Salesforce AI Associate AI Associate study guide
Full concept coverage aligned to exam objectives
- →
AI Associate practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI Associate practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Ethical AI and Data Privacy practice questions
Practise AI Associate questions linked to Ethical AI and Data Privacy.
Salesforce Einstein AI Features practice questions
Practise AI Associate questions linked to Salesforce Einstein AI Features.
AI Fundamentals practice questions
Practise AI Associate questions linked to AI Fundamentals.
AI Capabilities in CRM practice questions
Practise AI Associate questions linked to AI Capabilities in CRM.
Ethical Considerations of AI practice questions
Practise AI Associate questions linked to Ethical Considerations of AI.
Data for AI practice questions
Practise AI Associate questions linked to Data for AI.
AI Associate fundamentals practice questions
Practise AI Associate questions linked to AI Associate fundamentals.
AI Associate scenario practice questions
Practise AI Associate questions linked to AI Associate scenario.
AI Associate troubleshooting practice questions
Practise AI Associate questions linked to AI Associate troubleshooting.
Practice this exam
Start a free AI Associate practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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 the 'Escalated__c' field has both 'True' and 'False' values in the training data. — Option A is correct because Einstein Prediction Builder requires the target prediction field to contain both positive and negative examples (e.g., 'True' and 'False') in the training data. Without both values, the model cannot learn the decision boundary between escalated and non-escalated cases, making binary classification impossible.
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.
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 →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.