- A
Remove sensitive attributes from the training data and avoid using proxies that strongly correlate with them
Removing sensitive attributes directly and also being cautious of proxy features minimizes the chance of the model indirectly using protected characteristics.
- B
Include all features and rely on the AI to ignore biased ones
Why wrong: Models can learn correlations with sensitive attributes even if they are not directly included; simply including them risks encoding bias.
- C
Apply a fairness constraint after training to adjust predictions
Why wrong: Post-hoc adjustments can help but are less principled than minimizing data use from the start. Data minimisation is proactive.
- D
Use differential privacy to add noise to the training data
Why wrong: Differential privacy protects individual records but does not address the inclusion of sensitive features; it is about privacy, not fairness.
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 data scientist is building a churn prediction model using Einstein Discovery. They want to ensure the model does not rely on sensitive attributes like race or gender, even if those are correlated with other features. Which technique is MOST aligned with Salesforce's data minimisation principle?
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
Remove sensitive attributes from the training data and avoid using proxies that strongly correlate with them
Data minimisation means using only the data necessary for the task. Excluding sensitive attributes from the feature set is the most direct way to prevent them from being used, even if they are correlated with other features. Correlation does not imply causation, and if those features are not essential, they should be removed.
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.
- ✓
Remove sensitive attributes from the training data and avoid using proxies that strongly correlate with them
Why this is correct
Removing sensitive attributes directly and also being cautious of proxy features minimizes the chance of the model indirectly using protected characteristics.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Include all features and rely on the AI to ignore biased ones
Why it's wrong here
Models can learn correlations with sensitive attributes even if they are not directly included; simply including them risks encoding bias.
- ✗
Apply a fairness constraint after training to adjust predictions
Why it's wrong here
Post-hoc adjustments can help but are less principled than minimizing data use from the start. Data minimisation is proactive.
- ✗
Use differential privacy to add noise to the training data
Why it's wrong here
Differential privacy protects individual records but does not address the inclusion of sensitive features; it is about privacy, not fairness.
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
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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: Remove sensitive attributes from the training data and avoid using proxies that strongly correlate with them — Data minimisation means using only the data necessary for the task. Excluding sensitive attributes from the feature set is the most direct way to prevent them from being used, even if they are correlated with other features. Correlation does not imply causation, and if those features are not essential, they should be removed.
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: 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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