- A
Add a fairness constraint to the model training
Fairness constraints adjust the model to reduce bias while maintaining accuracy.
- B
Lower the decision threshold for minority groups
Why wrong: This is a form of differential treatment that may be considered discriminatory.
- C
Remove zip code and employment history from the model
Why wrong: Removing features may not eliminate bias and could reduce model accuracy.
- D
Continue using the model as is, since data is balanced
Why wrong: The model still shows disparate impact, which is unethical.
Question 301 of 1,000
AI Associate Ethical Considerations of AI Practice Question
This AI Associate practice question tests your understanding of ethical considerations of ai. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 large financial institution uses Einstein Discovery to automate loan pre-approval decisions. The model was trained on ten years of historical data. After deployment, the compliance team finds that the approval rate for minority groups is 15% lower than the majority group, even after controlling for credit score and income. The data is balanced across groups. The model uses features like zip code, employment history, and debt-to-income ratio. The institution has a strict policy of fairness and non-discrimination. The AI team proposes three options: (1) remove zip code and employment history from the model, (2) add a fairness constraint to the model training, (3) lower the decision threshold for minority groups to balance approval rates. The compliance officer must choose the most ethical and effective course of action that aligns with Salesforce AI ethical guidelines. Which option should they choose?
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
✓
Add a fairness constraint to the model training
Option A is the correct choice because adding a fairness constraint during model training directly addresses the disparate impact on minority groups without resorting to post-hoc discriminatory adjustments or removing predictive features. Salesforce AI ethical guidelines emphasize proactive fairness in model development, such as incorporating fairness constraints, rather than reactive changes that could introduce new biases or reduce model utility. Option B (lowering the threshold for minority groups) is unethical as it applies different standards to protected groups, which could violate anti-discrimination laws and Salesforce's principle of treating all individuals fairly. Option C (removing zip code and employment history) may not eliminate bias because other correlated features (e.g., debt-to-income ratio) can still act as proxies, and it could reduce predictive accuracy without guaranteeing fairness. Option D (continuing as is) ignores the documented disparate impact, violating the institution's non-discrimination policy and ethical AI practices.
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.
- ✓
Add a fairness constraint to the model training
Why this is correct
Fairness constraints adjust the model to reduce bias while maintaining accuracy.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Lower the decision threshold for minority groups
Why it's wrong here
This is a form of differential treatment that may be considered discriminatory.
- ✗
Remove zip code and employment history from the model
Why it's wrong here
Removing features may not eliminate bias and could reduce model accuracy.
- ✗
Continue using the model as is, since data is balanced
Why it's wrong here
The model still shows disparate impact, which is unethical.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Command / output trap
The model still shows disparate impact, which is unethical.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 AI Associate exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this AI Associate question test?
Ethical Considerations of AI — This question tests Ethical Considerations of AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Add a fairness constraint to the model training — Option A is the correct choice because adding a fairness constraint during model training directly addresses the disparate impact on minority groups without resorting to post-hoc discriminatory adjustments or removing predictive features. Salesforce AI ethical guidelines emphasize proactive fairness in model development, such as incorporating fairness constraints, rather than reactive changes that could introduce new biases or reduce model utility. Option B (lowering the threshold for minority groups) is unethical as it applies different standards to protected groups, which could violate anti-discrimination laws and Salesforce's principle of treating all individuals fairly. Option C (removing zip code and employment history) may not eliminate bias because other correlated features (e.g., debt-to-income ratio) can still act as proxies, and it could reduce predictive accuracy without guaranteeing fairness. Option D (continuing as is) ignores the documented disparate impact, violating the institution's non-discrimination policy and ethical AI practices.
What should I do if I get this AI Associate question wrong?
Identify which AI Associate exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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: Jun 23, 2026
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