Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A company develops an AI system to screen job candidates based on their resumes. The system is trained on historical data. Analysis reveals that the model has an adverse impact against female candidates due to a proxy feature (e.g., 'years of continuous employment') that correlates with gender. The team removes the protected attribute 'gender' from the training data but the biased outcome persists. According to Microsoft's responsible AI principles, which additional step should the team take to address this unfairness?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Distractor review
Remove the offending proxy feature 'years of continuous employment' from the training data.
While removing one proxy feature may help, other proxy features might still encode the bias, making this solution incomplete.
B
Best answer
Use a tool like Fairlearn to detect and mitigate the bias while maintaining model performance.
Fairlearn provides algorithms and metrics to detect and mitigate unfairness, directly addressing the persistent bias even after removing protected attributes.
C
Distractor review
Train a separate model for each gender group to ensure equal outcomes.
Training separate models can lead to separate and potentially unequal outcomes, and it does not align with the fairness principle of treating all groups equitably.
D
Distractor review
Collect more training data from underrepresented groups.
Collecting more data can help reduce bias in the long term, but it does not address the existing biased outcomes from the current model.
Common exam trap
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.
Technical deep dive
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.
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
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FAQ
Questions learners often ask
What does this AI-900 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Use a tool like Fairlearn to detect and mitigate the bias while maintaining model performance. — Removing the protected attribute often fails because proxy features can encode the same bias. Microsoft's Fairness principle requires actively detecting and mitigating bias using specialized tools. Fairlearn is a Microsoft tool designed for this purpose. Removing the proxy feature may help but other proxies could remain. Training separate models for each group may lead to unequal treatment and does not directly address fairness. Collecting more data does not fix the existing bias in the model. Therefore, using a fairness tool like Fairlearn is the best approach.
What should I do if I get this AI-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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