- A
Empathy
Why wrong: Empathy is about designing for human benefit, but the core issue here is erroneous scoring.
- B
Safety
Why wrong: Safety focuses on preventing harmful outputs, which is not directly the issue here.
- C
Accuracy
Accuracy requires models to be accurate and tested; biased predictions show the model is not accurate for that region.
- D
Transparency
Why wrong: Transparency is about explaining AI decisions, not about correctness of predictions across segments.
Question 268 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 sales operations manager notices that the AI-driven lead scoring model assigns lower scores to leads from a particular region, even though those leads historically convert at a higher rate. Which Salesforce Trusted AI principle is most directly violated?
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
✓
Accuracy
The AI-driven lead scoring model is producing outputs that do not match the ground truth (historical conversion rates), which is a direct failure of the Accuracy principle. Accuracy requires that AI systems perform as intended and produce reliable, correct predictions; here, the model's scores are systematically wrong for a specific region, violating that requirement.
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.
- ✗
Empathy
Why it's wrong here
Empathy is about designing for human benefit, but the core issue here is erroneous scoring.
- ✗
Safety
Why it's wrong here
Safety focuses on preventing harmful outputs, which is not directly the issue here.
- ✓
Accuracy
Why this is correct
Accuracy requires models to be accurate and tested; biased predictions show the model is not accurate for that region.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Transparency
Why it's wrong here
Transparency is about explaining AI decisions, not about correctness of predictions across segments.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between Accuracy (output correctness) and Transparency (explainability), leading candidates to confuse a model giving wrong scores with a model lacking explanation for its scores.
Trap categories for this question
Command / output trap
Safety focuses on preventing harmful outputs, which is not directly the issue here.
Detailed technical explanation
How to think about this question
Under the hood, accuracy in AI models is measured by metrics like precision, recall, and F1-score against a validation set. A systematic bias against a region indicates a data distribution shift or feature encoding error—for example, the model may have learned a spurious correlation from imbalanced training data where that region was underrepresented. In real-world Salesforce deployments, such a violation would trigger a model audit to rebalance training data or adjust feature weights.
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: Accuracy — The AI-driven lead scoring model is producing outputs that do not match the ground truth (historical conversion rates), which is a direct failure of the Accuracy principle. Accuracy requires that AI systems perform as intended and produce reliable, correct predictions; here, the model's scores are systematically wrong for a specific region, violating that requirement.
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
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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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