- A
Exclude gender from the feature set used for model training
Excluding protected attributes is a direct way to prevent the model from using them; it aligns with data minimisation.
- B
Use gender as a feature but ignore the model predictions for certain groups
Why wrong: Ignoring predictions is not a systematic solution; the model will still be biased.
- C
Allow gender in training but use a post-processing technique to adjust scores
Why wrong: Post-processing can mitigate but not eliminate bias; exclusion is more straightforward.
- D
Include gender as a feature and then apply a fairness constraint during training
Why wrong: While possible, the simpler and more common approach is to exclude the feature entirely to avoid bias.
Question 716 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. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 data scientist is building a custom AI model using Salesforce Data Cloud to predict customer churn. They want to ensure that the model does not inadvertently use gender as a feature to avoid biased predictions. Which step is MOST appropriate?
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
✓
Exclude gender from the feature set used for model training
The best practice is to exclude protected attributes from the model features. Data minimisation supports this. Auditing for bias is also important but does not prevent the model from using the attribute in the first place.
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.
- ✓
Exclude gender from the feature set used for model training
Why this is correct
Excluding protected attributes is a direct way to prevent the model from using them; it aligns with data minimisation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use gender as a feature but ignore the model predictions for certain groups
Why it's wrong here
Ignoring predictions is not a systematic solution; the model will still be biased.
- ✗
Allow gender in training but use a post-processing technique to adjust scores
Why it's wrong here
Post-processing can mitigate but not eliminate bias; exclusion is more straightforward.
- ✗
Include gender as a feature and then apply a fairness constraint during training
Why it's wrong here
While possible, the simpler and more common approach is to exclude the feature entirely to avoid bias.
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.
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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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: Exclude gender from the feature set used for model training — The best practice is to exclude protected attributes from the model features. Data minimisation supports this. Auditing for bias is also important but does not prevent the model from using the attribute in the first place.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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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