- A
Use the score factors feature to show top predictors
Score factors show the key fields driving the prediction in an accessible way.
- B
Share the full training dataset for transparency
Why wrong: Sharing the dataset raises privacy concerns and is not an explanation.
- C
Allow stakeholders to retrain the model themselves
Why wrong: Retraining does not explain existing predictions.
- D
Provide the raw model weights and coefficients
Why wrong: Raw weights are too technical for non-technical audiences.
Question 809 of 1,000
AI Associate Ethical AI and Data Privacy Practice Question
This AI Associate practice question tests your understanding of ethical ai and data privacy. 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 predict customer churn. They want to ensure the predictions are explainable to non-technical stakeholders. What is the best way to provide explanation?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
✓
Use the score factors feature to show top predictors
Option A is correct because Einstein Discovery's score factors feature explicitly lists the top predictors and their contributions to each prediction, making the model's reasoning transparent and accessible to non-technical stakeholders. This aligns with the need for explainable AI (XAI) without requiring deep data science knowledge.
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.
- ✓
Use the score factors feature to show top predictors
Why this is correct
Score factors show the key fields driving the prediction in an accessible way.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Share the full training dataset for transparency
Why it's wrong here
Sharing the dataset raises privacy concerns and is not an explanation.
- ✗
Allow stakeholders to retrain the model themselves
Why it's wrong here
Retraining does not explain existing predictions.
- ✗
Provide the raw model weights and coefficients
Why it's wrong here
Raw weights are too technical for non-technical audiences.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that providing raw data or model internals (like weights) constitutes explainability, when in fact non-technical stakeholders need simplified, instance-level explanations like score factors.
Detailed technical explanation
How to think about this question
Score factors in Einstein Discovery are derived from the model's feature importance values, often using Shapley values or permutation importance to quantify each predictor's marginal contribution to the prediction. Under the hood, the platform computes these contributions per prediction instance, enabling granular explanations that can be visualized as bar charts or tables. In a real-world churn scenario, score factors might reveal that 'days since last login' contributes +0.3 to churn probability, while 'support tickets in last 30 days' contributes -0.1, giving stakeholders actionable insights without needing to interpret model internals.
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.
- →
Ethical AI and Data Privacy — study guide chapter
Learn the concepts, then practise the questions
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FAQ
Questions learners often ask
What does this AI Associate question test?
Ethical AI and Data Privacy — This question tests Ethical AI and Data Privacy — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the score factors feature to show top predictors — Option A is correct because Einstein Discovery's score factors feature explicitly lists the top predictors and their contributions to each prediction, making the model's reasoning transparent and accessible to non-technical stakeholders. This aligns with the need for explainable AI (XAI) without requiring deep data science knowledge.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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