Quick Answer
The answer is historical case records including resolution time. This is the most critical data for training an Einstein Prediction Builder model because it relies on supervised machine learning, which requires labeled examples—known outcomes—to identify patterns and make accurate predictions. Without historical records that include the actual resolution time, the model has no ground truth to learn from, rendering it unable to forecast future case durations. On the Salesforce AI Associate exam, this concept tests your understanding that Prediction Builder is a supervised tool, not an unsupervised one; a common trap is choosing generic case data without outcomes or focusing on real-time fields instead of historical records. Remember the key principle: for any prediction, you need past outcomes to teach the model—think of it as “no label, no learn.”
This AI Associate practice question tests your understanding of data for ai. 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 wants to use Einstein Prediction Builder to predict case resolution time. What type of data is most critical for training this model?
Customer satisfaction survey responses
Why wrong: Surveys measure satisfaction, not resolution time.
Historical case records including resolution time
Historical data is essential for training.
Product inventory levels
Why wrong: Inventory does not directly affect resolution time.
Employee work schedules
Why wrong: Schedules may be a feature but not the most critical.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Historical case records including resolution time
Einstein Prediction Builder requires historical data with known outcomes to train a supervised machine learning model. Historical case records containing actual resolution times provide the labeled examples needed for the model to learn patterns and predict future case resolution times. Without this ground truth data, the model cannot be trained to make accurate predictions.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Customer satisfaction survey responses
Why it's wrong here
Surveys measure satisfaction, not resolution time.
Historical case records including resolution time
Why this is correct
Historical data is essential for training.
Related concept
Read the scenario before looking for a memorised answer.
Product inventory levels
Why it's wrong here
Inventory does not directly affect resolution time.
Employee work schedules
Why it's wrong here
Schedules may be a feature but not the most critical.
Common exam traps
The trap here is that candidates may confuse factors that influence resolution time (like employee schedules or inventory) with the actual labeled outcome data required to train a supervised prediction model.
Detailed technical explanation
Einstein Prediction Builder uses automated machine learning (AutoML) to select the best algorithm (e.g., gradient boosting, linear regression) based on the training data. The model learns correlations between case attributes (e.g., priority, type, source) and the target field (resolution time) from historical records. A common subtlety is that the training data must include cases with complete resolution timestamps; missing or inconsistent data can lead to biased predictions or model failure.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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FAQ
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
The correct answer is: Historical case records including resolution time — Einstein Prediction Builder requires historical data with known outcomes to train a supervised machine learning model. Historical case records containing actual resolution times provide the labeled examples needed for the model to learn patterns and predict future case resolution times. Without this ground truth data, the model cannot be trained to make accurate predictions.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 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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