- A
Disable the AI model and make all decisions manually
Why wrong: Overly drastic; bias can be mitigated with proper controls.
- B
Audit the model for bias, provide transparency on decision factors, and require human review for denied applications
This approach aligns with accuracy, transparency, and empathy principles.
- C
Remove demographic data from the model entirely and continue using it
Why wrong: Removing demographic data does not guarantee fairness; bias can still exist through proxy variables.
- D
Retrain the model with more data from the affected group and deploy automatically
Why wrong: Simply retraining may not solve bias; oversight and transparency are also needed.
Question 375 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.
An AI model predicts loan approvals, and the bank notices that the model disproportionately denies loans to a certain demographic group. Which combination of actions addresses the AI bias according to Salesforce's Trusted AI principles?
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
✓
Audit the model for bias, provide transparency on decision factors, and require human review for denied applications
Option B is correct because it aligns with Salesforce's Trusted AI principles, which emphasize accountability, transparency, and human oversight. Auditing the model for bias identifies disparities, providing transparency on decision factors ensures stakeholders understand how outcomes are determined, and requiring human review for denied applications introduces a safeguard against automated discrimination. This combination addresses bias without abandoning AI's benefits.
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.
- ✗
Disable the AI model and make all decisions manually
Why it's wrong here
Overly drastic; bias can be mitigated with proper controls.
- ✓
Audit the model for bias, provide transparency on decision factors, and require human review for denied applications
Why this is correct
This approach aligns with accuracy, transparency, and empathy principles.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Remove demographic data from the model entirely and continue using it
Why it's wrong here
Removing demographic data does not guarantee fairness; bias can still exist through proxy variables.
- ✗
Retrain the model with more data from the affected group and deploy automatically
Why it's wrong here
Simply retraining may not solve bias; oversight and transparency are also needed.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think removing demographic data (Option C) is sufficient to eliminate bias, but they overlook that proxy variables can perpetuate discrimination, and Salesforce's principles require proactive auditing and transparency, not just data sanitization.
Detailed technical explanation
How to think about this question
Under the hood, bias in AI models often stems from historical data imbalances or spurious correlations, which can persist even after removing protected attributes due to proxy features. Salesforce's Trusted AI framework recommends a three-pillar approach: detect (audit), explain (transparency), and act (human review), which aligns with regulatory standards like the EU AI Act's high-risk classification. In practice, a bank might use SHAP values to explain feature importance and set up a human-in-the-loop (HITL) workflow for denied applications to ensure fairness.
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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Ethical AI and Data Privacy — study guide chapter
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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: Audit the model for bias, provide transparency on decision factors, and require human review for denied applications — Option B is correct because it aligns with Salesforce's Trusted AI principles, which emphasize accountability, transparency, and human oversight. Auditing the model for bias identifies disparities, providing transparency on decision factors ensures stakeholders understand how outcomes are determined, and requiring human review for denied applications introduces a safeguard against automated discrimination. This combination addresses bias without abandoning AI's benefits.
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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