- A
Report the suspicious lead and request a model audit for bias
Reporting ensures the issue is logged and the model can be checked for bias on email/phone features.
- B
Delete the lead from the CRM to avoid influencing future predictions
Why wrong: Deleting data removes evidence and does not address the underlying model bias.
- C
Manually override the score to 50 and move on
Why wrong: Manual override without investigation does not fix the root cause and may reintroduce bias.
- D
Review the score factors to see which fields contributed most to the 95 score
Score factors explain the model's decision, revealing if invalid fields were weighted too heavily.
- E
Contact Salesforce support to disable the AI model
Why wrong: Disabling the model is drastic; the rep should first investigate and escalate.
AI Associate Ethical AI and Data Privacy Practice Question
This AI Associate practice question tests your understanding of ethical ai and data privacy. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 rep receives an AI-generated lead score of 95, but the rep notices the lead's email domain is 'example.com' and the phone number is invalid. The rep suspects the AI model is overvaluing certain features. Which TWO actions should the rep take to investigate and address the issue?
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
Report the suspicious lead and request a model audit for bias
Option A is correct because reporting suspicious leads and requesting a model audit is the proper governance procedure when an AI model produces outputs that contradict observable data. This triggers a review of the model's feature weights and training data to identify bias or overvaluation of specific fields, ensuring the model remains reliable and compliant with ethical AI standards.
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.
- ✓
Report the suspicious lead and request a model audit for bias
Why this is correct
Reporting ensures the issue is logged and the model can be checked for bias on email/phone features.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Delete the lead from the CRM to avoid influencing future predictions
Why it's wrong here
Deleting data removes evidence and does not address the underlying model bias.
- ✗
Manually override the score to 50 and move on
Why it's wrong here
Manual override without investigation does not fix the root cause and may reintroduce bias.
- ✓
Review the score factors to see which fields contributed most to the 95 score
Why this is correct
Score factors explain the model's decision, revealing if invalid fields were weighted too heavily.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Contact Salesforce support to disable the AI model
Why it's wrong here
Disabling the model is drastic; the rep should first investigate and escalate.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between reactive manual fixes (like overriding or deleting) and proper investigative governance actions (like auditing and reviewing feature contributions), tempting candidates to choose quick fixes that violate data integrity or model management best practices.
Detailed technical explanation
How to think about this question
Under the hood, AI lead scoring models use feature importance metrics (e.g., SHAP values or permutation importance) to determine which fields most influence the score. A score of 95 despite invalid contact data suggests the model may be overfitting to features like email domain or lead source, which can be detected by reviewing the model's feature attribution report. In practice, a model audit would involve checking training data for label leakage or biased sampling, then retraining with corrected weights or adding validation rules to flag low-quality leads.
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: Report the suspicious lead and request a model audit for bias — Option A is correct because reporting suspicious leads and requesting a model audit is the proper governance procedure when an AI model produces outputs that contradict observable data. This triggers a review of the model's feature weights and training data to identify bias or overvaluation of specific fields, ensuring the model remains reliable and compliant with ethical AI standards.
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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