- A
Stories that explain key factors in natural language
Stories are automatically generated narratives highlighting important findings.
- B
Operational prescriptions that recommend specific actions
Improvement suggestions and operational prescriptions provide actionable steps.
- C
Waterfall charts illustrating the contribution of each factor
Waterfall charts show how each factor influences the outcome.
- D
Sentiment analysis scores
Why wrong: Sentiment analysis is not a standard output of Einstein Discovery.
- E
Custom binary prediction models
Why wrong: Prediction models are built by Einstein Prediction Builder, not Discovery.
Question 110 of 1,000
AI Associate Salesforce Einstein AI Features Practice Question
This AI Associate practice question tests your understanding of salesforce einstein ai features. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 uses Einstein Discovery to analyze sales data. They want to understand the key drivers of deal closures. Which THREE output types can Einstein Discovery provide? (Choose 3)
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
✓
Stories that explain key factors in natural language
Option A is correct because Einstein Discovery generates 'Stories' that automatically describe key factors influencing outcomes in natural language, enabling users to understand drivers of deal closures without manual analysis. These stories are derived from statistical models that identify the most impactful variables in the dataset.
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.
- ✓
Stories that explain key factors in natural language
Why this is correct
Stories are automatically generated narratives highlighting important findings.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Operational prescriptions that recommend specific actions
Why this is correct
Improvement suggestions and operational prescriptions provide actionable steps.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Waterfall charts illustrating the contribution of each factor
Why this is correct
Waterfall charts show how each factor influences the outcome.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Sentiment analysis scores
Why it's wrong here
Sentiment analysis is not a standard output of Einstein Discovery.
- ✗
Custom binary prediction models
Why it's wrong here
Prediction models are built by Einstein Prediction Builder, not Discovery.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the distinct output types of Einstein Discovery (Stories, prescriptions, waterfall charts) with other Einstein AI features like sentiment analysis or custom model builders, leading them to select options that are valid Einstein capabilities but not outputs of Discovery.
Trap categories for this question
Command / output trap
Sentiment analysis is not a standard output of Einstein Discovery.
Detailed technical explanation
How to think about this question
Einstein Discovery uses automated machine learning (AutoML) to train regression or classification models on historical data, then outputs three primary result types: Stories (natural language explanations), prescriptions (recommended actions to improve outcomes), and waterfall charts (visual breakdown of each factor's contribution to the predicted outcome). The waterfall chart shows the additive impact of each driver, starting from the baseline prediction, which is critical for understanding how specific variables like discount percentage or lead source influence deal closure probability.
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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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: Stories that explain key factors in natural language — Option A is correct because Einstein Discovery generates 'Stories' that automatically describe key factors influencing outcomes in natural language, enabling users to understand drivers of deal closures without manual analysis. These stories are derived from statistical models that identify the most impactful variables in the dataset.
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.
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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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