- A
Waterfall charts showing contribution of each variable
Waterfall charts are part of the statistical analysis output, showing driver contributions.
- B
A story narrative explaining key insights
Einstein Discovery generates stories that explain insights in plain language.
- C
Prediction scores for each record
Why wrong: Prediction scores are output of Prediction Builder, not Discovery.
- D
A trained model for deployment
Why wrong: Discovery does not produce deployable models; it provides analysis and recommendations.
- E
Improvement suggestions with expected impact
Discovery provides actionable improvement suggestions with projected impact.
Question 374 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 data analyst is using Einstein Discovery to analyze customer churn. They want to understand the key drivers of churn and get actionable recommendations. Which THREE outputs does Einstein Discovery provide to meet this need?
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
✓
Waterfall charts showing contribution of each variable
Waterfall charts in Einstein Discovery visually decompose the contribution of each predictor variable to the overall prediction, showing how much each driver increases or decreases the likelihood of churn. This directly helps the analyst identify the key drivers of churn, meeting the requirement to understand what factors are most influential.
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.
- ✓
Waterfall charts showing contribution of each variable
Why this is correct
Waterfall charts are part of the statistical analysis output, showing driver contributions.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
A story narrative explaining key insights
Why this is correct
Einstein Discovery generates stories that explain insights in plain language.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Prediction scores for each record
Why it's wrong here
Prediction scores are output of Prediction Builder, not Discovery.
- ✗
A trained model for deployment
Why it's wrong here
Discovery does not produce deployable models; it provides analysis and recommendations.
- ✓
Improvement suggestions with expected impact
Why this is correct
Discovery provides actionable improvement suggestions with projected impact.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse raw prediction outputs (scores per record) or deployment artifacts (trained models) with the interpretability and recommendation outputs that Einstein Discovery specifically surfaces for business users, such as waterfall charts, narratives, and improvement suggestions.
Trap categories for this question
Command / output trap
Prediction scores are output of Prediction Builder, not Discovery.
Detailed technical explanation
How to think about this question
Einstein Discovery uses automated machine learning to generate a global explanation model (e.g., Shapley value decomposition) that produces waterfall charts, showing the additive contribution of each feature from a baseline prediction. The story narrative is generated via natural language generation (NLG) that summarizes the most influential segments and trends, while improvement suggestions are derived from counterfactual analysis, indicating which changes to a specific variable would have the greatest impact on reducing churn 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: Waterfall charts showing contribution of each variable — Waterfall charts in Einstein Discovery visually decompose the contribution of each predictor variable to the overall prediction, showing how much each driver increases or decreases the likelihood of churn. This directly helps the analyst identify the key drivers of churn, meeting the requirement to understand what factors are most influential.
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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