- A
Remove the offending proxy feature 'years of continuous employment' from the training data.
Why wrong: While removing one proxy feature may help, other proxy features might still encode the bias, making this solution incomplete.
- B
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
Train a separate model for each gender group to ensure equal outcomes.
Why wrong: 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
Collect more training data from underrepresented groups.
Why wrong: Collecting more data can help reduce bias in the long term, but it does not address the existing biased outcomes from the current model.
Quick Answer
The correct answer is to use a tool like Fairlearn to detect and mitigate the bias while maintaining model performance. This is necessary because the fairness principle in responsible AI requires addressing proxy features—such as 'years of continuous employment'—that correlate with protected attributes like gender and perpetuate adverse impact even after the attribute itself is removed. Fairlearn is an open-source toolkit that provides algorithms to assess and reduce disparity without simply discarding predictive data, which aligns with Microsoft’s guidance for bias mitigation. On the AI-900 exam, this tests your understanding that fairness goes beyond removing obvious protected attributes; a common trap is assuming removal alone suffices, when proxy features must be actively detected and mitigated. Remember the mnemonic “Proxy Persists, Fairlearn Fixes” to recall that bias can hide in correlated features and requires specialized tools to correct.
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
This AI-900 practice question tests your understanding of describe artificial intelligence workloads and considerations. 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 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
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use a tool like Fairlearn to detect and mitigate the bias while maintaining model performance.
Option B is correct because Microsoft's responsible AI principle of fairness requires not just removing protected attributes but also detecting and mitigating proxy features that cause bias. Fairlearn is a Microsoft open-source toolkit specifically designed to assess and mitigate unfairness in AI systems, offering algorithms like 'Exponentiated Gradient Reduction' or 'Grid Search' to reduce disparity while preserving model performance. Simply removing the proxy feature (A) may not always be feasible if it carries predictive value, and Fairlearn provides a systematic way to balance fairness and accuracy.
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 the offending proxy feature 'years of continuous employment' from the training data.
Why it's wrong here
While removing one proxy feature may help, other proxy features might still encode the bias, making this solution incomplete.
- ✓
Use a tool like Fairlearn to detect and mitigate the bias while maintaining model performance.
Why this is correct
Fairlearn provides algorithms and metrics to detect and mitigate unfairness, directly addressing the persistent bias even after removing protected attributes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Train a separate model for each gender group to ensure equal outcomes.
Why it's wrong here
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.
- ✗
Collect more training data from underrepresented groups.
Why it's wrong here
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 traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume removing the protected attribute (gender) alone solves fairness, but Microsoft's responsible AI principles emphasize that proxy features can perpetuate bias, requiring tools like Fairlearn for detection and mitigation rather than simplistic feature removal or data collection.
Detailed technical explanation
How to think about this question
Fairlearn provides metrics like 'demographic parity difference' and 'equalized odds difference' to quantify bias, and its mitigation algorithms (e.g., 'Exponentiated Gradient Reduction') work by adding fairness constraints during training, adjusting the model's decision boundary to reduce disparity. In practice, proxy features like 'years of continuous employment' can encode historical discrimination (e.g., career gaps due to maternity leave), and Fairlearn can detect such correlations even when the protected attribute is removed. A real-world scenario is a hiring model where 'total years of experience' correlates with gender due to societal biases; Fairlearn can reduce the disparate impact without discarding the feature entirely.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe Artificial Intelligence workloads and considerations — This question tests Describe Artificial Intelligence workloads and considerations — 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. — Option B is correct because Microsoft's responsible AI principle of fairness requires not just removing protected attributes but also detecting and mitigating proxy features that cause bias. Fairlearn is a Microsoft open-source toolkit specifically designed to assess and mitigate unfairness in AI systems, offering algorithms like 'Exponentiated Gradient Reduction' or 'Grid Search' to reduce disparity while preserving model performance. Simply removing the proxy feature (A) may not always be feasible if it carries predictive value, and Fairlearn provides a systematic way to balance fairness and accuracy.
What should I do if I get this AI-900 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.
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 →
Same concept, more angles
1 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A retail company uses an AI system to predict customer churn based on demographic and behavioral data. The team discovers that the model gives disproportionately higher churn predictions for customers from a particular zip code, even when their behavior is similar to others. Which Microsoft responsible AI principle is most directly relevant to addressing this issue?
easy- A.Transparency
- ✓ B.Fairness
- C.Reliability and Safety
- D.Privacy and Security
Why B: The model's disproportionate churn predictions for a specific zip code, despite similar behavior, indicates a bias that unfairly impacts that group. Microsoft's Fairness principle directly addresses this by requiring AI systems to treat all groups equitably and avoid discrimination based on sensitive attributes like location. Ensuring fairness involves auditing training data and model outputs for such disparities and applying mitigation techniques.
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Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft 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-900 exam.
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