- A
Remove protected attributes from training data
Removing attributes like race/gender can prevent direct discrimination.
- B
Focus training on majority group data for accuracy
Why wrong: This would worsen bias against minorities.
- C
Randomize a portion of model outputs
Why wrong: Randomization does not address underlying bias.
- D
Use diverse and representative training data
Diverse data reduces underrepresentation.
- E
Apply fairness metrics during model evaluation
Fairness metrics help detect and reduce bias.
Question 238 of 1,000
AI Associate Ethical Considerations of AI Practice Question
This AI Associate practice question tests your understanding of ethical considerations of ai. 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.
Which THREE strategies can help mitigate bias in an AI model? (Choose three.)
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 protected attributes from training data
Option A is correct because removing protected attributes (e.g., race, gender) from training data reduces the risk of the model directly learning correlations with these sensitive features. This is a common pre-processing technique to prevent direct discrimination, though it may not eliminate indirect bias if correlated proxy features remain.
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 protected attributes from training data
Why this is correct
Removing attributes like race/gender can prevent direct discrimination.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Focus training on majority group data for accuracy
Why it's wrong here
This would worsen bias against minorities.
- ✗
Randomize a portion of model outputs
Why it's wrong here
Randomization does not address underlying bias.
- ✓
Use diverse and representative training data
Why this is correct
Diverse data reduces underrepresentation.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Apply fairness metrics during model evaluation
Why this is correct
Fairness metrics help detect and reduce bias.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the misconception that simply removing protected attributes or randomizing outputs is sufficient to eliminate bias, when in fact bias can persist through proxies and requires comprehensive fairness evaluation and diverse data.
Detailed technical explanation
How to think about this question
Bias mitigation often involves a combination of pre-processing (e.g., reweighting, data augmentation), in-processing (e.g., adversarial debiasing, fairness constraints), and post-processing (e.g., equalized odds calibration). For example, applying fairness metrics like demographic parity or equal opportunity during evaluation helps quantify disparities, enabling targeted adjustments. Real-world scenarios, such as hiring algorithms, show that even after removing protected attributes, proxy variables like zip code or education level can perpetuate bias, requiring iterative testing and threshold tuning.
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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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: Remove protected attributes from training data — Option A is correct because removing protected attributes (e.g., race, gender) from training data reduces the risk of the model directly learning correlations with these sensitive features. This is a common pre-processing technique to prevent direct discrimination, though it may not eliminate indirect bias if correlated proxy features remain.
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.
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Last reviewed: Jun 30, 2026
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