- A
Automated A/B test designs
Why wrong: Not a feature of Einstein Discovery.
- B
Improvement suggestions to increase the target metric
Correct.
- C
Stories that explain key drivers in natural language
Correct.
- D
Waterfall charts showing the impact of different factors on the outcome
Correct.
- E
Hypothesis testing results for statistical significance
Why wrong: Not a standard output of Discovery.
Question 27 of 1,000
Einstein Discovery Outputs
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 is using Einstein Discovery to analyze sales data and improve win rates. Which THREE outputs does Einstein Discovery provide?
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
✓
Improvement suggestions to increase the target metric
Option B is correct because Einstein Discovery provides actionable improvement suggestions that directly target the metric being analyzed, such as win rate. These suggestions are derived from the model's analysis of historical data and are designed to help users take specific actions to improve outcomes.
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.
- ✗
Automated A/B test designs
Why it's wrong here
Not a feature of Einstein Discovery.
- ✓
Improvement suggestions to increase the target metric
Why this is correct
Correct.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Stories that explain key drivers in natural language
Why this is correct
Correct.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Waterfall charts showing the impact of different factors on the outcome
Why this is correct
Correct.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Hypothesis testing results for statistical significance
Why it's wrong here
Not a standard output of Discovery.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the distinction between predictive/prescriptive analytics and traditional statistical methods, leading candidates to mistakenly select hypothesis testing (Option E) when Einstein Discovery actually uses machine learning-based feature importance and natural language generation.
Trap categories for this question
Command / output trap
Not a standard output of Discovery.
Detailed technical explanation
How to think about this question
Einstein Discovery uses automated machine learning (AutoML) to build regression or classification models on the input data, then applies Shapley value decomposition to quantify the contribution of each feature to the predicted outcome. The 'Stories' feature (Option C) generates natural language explanations of these key drivers, while the waterfall charts (Option D) visually break down the additive impact of each factor, making the model's reasoning transparent and actionable for business users.
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: Improvement suggestions to increase the target metric — Option B is correct because Einstein Discovery provides actionable improvement suggestions that directly target the metric being analyzed, such as win rate. These suggestions are derived from the model's analysis of historical data and are designed to help users take specific actions to improve outcomes.
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 →
Same concept, more angles
3 more ways this is tested on AI Associate
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist wants to use Einstein Discovery to analyze customer churn. They want to understand which factors contribute most to churn and get actionable suggestions. Which THREE outputs does Einstein Discovery provide?
hard- A.Prediction score for each record
- ✓ B.Improvement suggestions (recommended actions)
- ✓ C.Story (narrative explanation of insights)
- ✓ D.Key drivers (most influential factors)
- E.Waterfall chart (visualization of step-by-step impact)
Why B: Option B is correct because Einstein Discovery provides improvement suggestions (recommended actions) that guide data scientists on specific changes to reduce churn. These actionable insights are generated from the predictive model's analysis of historical data, offering concrete steps like 'increase engagement frequency' or 'offer discount' to improve outcomes.
Variation 2. 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)
medium- ✓ A.Stories that explain key factors in natural language
- ✓ B.Operational prescriptions that recommend specific actions
- ✓ C.Waterfall charts illustrating the contribution of each factor
- D.Sentiment analysis scores
- E.Custom binary prediction models
Why A: 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.
Variation 3. A company uses Einstein Discovery to analyze their sales pipeline. They see a waterfall chart showing the expected revenue changes from one stage to the next. What does the waterfall chart primarily help identify?
medium- ✓ A.The expected revenue at each stage and the changes between stages.
- B.The accuracy of the AI prediction compared to actuals.
- C.The improvement suggestions from Einstein Discovery.
- D.The top reasons why deals are won or lost.
Why A: In Einstein Discovery, waterfall charts visualize the contribution of individual factors to a target metric (e.g., revenue). They show how each stage or factor adds or subtracts from the total, helping identify key drivers.
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Last reviewed: Jul 4, 2026
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