- A
Increasing model depth
Why wrong: Can amplify bias.
- B
Removing all sensitive attributes
Why wrong: May not eliminate bias due to proxy variables.
- C
Re-weighting training samples
Adjusts for imbalanced representation.
- D
Adding regularization
Why wrong: Does not directly mitigate bias.
- E
Using adversarial debiasing
Learns unbiased representations.
Question 535 of 1,000
AI Associate Reweighting 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. A key principle to apply: reweighting. 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 TWO approaches are recommended for mitigating bias in AI models?
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
✓
Re-weighting training samples
Option C is correct because re-weighting training samples adjusts for imbalanced representation, reducing bias. Option E is correct because adversarial debiasing learns unbiased representations by minimizing a classifier's ability to predict sensitive attributes. Option A is incorrect because increasing model depth can amplify bias rather than mitigate it. Option B is incorrect because removing all sensitive attributes may not eliminate bias due to proxy variables. Option D is incorrect because regularization, while useful for overfitting, does not directly target bias.
Key principle: Reweighting
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increasing model depth
Why it's wrong here
Can amplify bias.
- ✗
Removing all sensitive attributes
Why it's wrong here
May not eliminate bias due to proxy variables.
- ✓
Re-weighting training samples
Why this is correct
Adjusts for imbalanced representation.
Related concept
Reweighting
- ✗
Adding regularization
Why it's wrong here
Does not directly mitigate bias.
- ✓
Using adversarial debiasing
Why this is correct
Learns unbiased representations.
Related concept
Reweighting
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
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Reweighting
- Adversarial debiasing
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
Reweighting
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. Reweighting 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.
Review reweighting, then practise related AI Associate questions on the same topic to reinforce the concept.
- →
Ethical Considerations of AI — study guide chapter
Learn the concepts, then practise the questions
- →
Ethical Considerations of AI practice questions
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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 — Reweighting.
What is the correct answer to this question?
The correct answer is: Re-weighting training samples — Option C is correct because re-weighting training samples adjusts for imbalanced representation, reducing bias. Option E is correct because adversarial debiasing learns unbiased representations by minimizing a classifier's ability to predict sensitive attributes. Option A is incorrect because increasing model depth can amplify bias rather than mitigate it. Option B is incorrect because removing all sensitive attributes may not eliminate bias due to proxy variables. Option D is incorrect because regularization, while useful for overfitting, does not directly target bias.
What should I do if I get this AI Associate question wrong?
Review reweighting, then practise related AI Associate questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Reweighting
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Last reviewed: Jun 23, 2026
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