The answer is at least one numeric field to predict. This is because Einstein Discovery story requirements center on a target variable that the AI can model, and without a numeric field—such as deal amount or probability score—the regression or classification algorithm has no outcome to learn from or uncover key drivers for. On the Salesforce AI Associate exam, this concept tests your understanding that Einstein Discovery is a predictive analytics tool, not a reporting dashboard; a common trap is assuming any field type will work, but the story engine specifically needs a continuous numeric target to generate insights. Remember the memory tip: “No number, no uncover”—if you don’t provide a numeric field to predict, the story simply cannot define what to analyze.
AI Associate AI Fundamentals Practice Question
This AI Associate practice question tests your understanding of ai fundamentals. 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 company wants to use Einstein Discovery to analyze sales data and automatically uncover key drivers of deal closure. What must the admin provide to create a story?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
At least one numeric field to predict
Einstein Discovery requires at least one numeric field as the prediction target (e.g., deal amount, probability score) to train its regression or classification model. Without a numeric field to predict, the story cannot define what outcome the AI should analyze or uncover key drivers for.
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.
✓
At least one numeric field to predict
Why this is correct
Discovery predicts numeric values or binary outcomes; a numeric target is required.
Related concept
Read the scenario before looking for a memorised answer.
✗
A date field for time series analysis
Why it's wrong here
Date fields are optional for time-based trends but not mandatory.
✗
A foreign key to relate objects
Why it's wrong here
Foreign keys are not required; Discovery works on a single dataset.
✗
A text field for sentiment analysis
Why it's wrong here
Sentiment analysis is not part of Discovery stories.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that Einstein Discovery requires a date field for time series or a foreign key for relational data, when in fact the core requirement is a numeric field to define the prediction target.
Detailed technical explanation
How to think about this question
Under the hood, Einstein Discovery uses automated machine learning (AutoML) to evaluate candidate models, requiring a target numeric field (for regression) or a binary numeric field (for classification, e.g., 0/1 for closed won/lost). The story generation process automatically selects the best algorithm (e.g., gradient boosting, linear regression) based on the target field's distribution and feature importance, without manual intervention.
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.
AI Fundamentals — This question tests AI Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: At least one numeric field to predict — Einstein Discovery requires at least one numeric field as the prediction target (e.g., deal amount, probability score) to train its regression or classification model. Without a numeric field to predict, the story cannot define what outcome the AI should analyze or uncover key drivers for.
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 →
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.
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.