Practice Questions

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Question 1

Which statement best describes fairness in an AI solution?

Answer: An AI solution should treat all individuals and groups equitably and avoid systematically disadvantaging specific populations.

Explanation: Fairness focuses on preventing biased outcomes that negatively affect certain groups, regardless of overall model accuracy.

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Question 2

An AI model accurately predicts loan approvals overall, but rejects applications from a specific demographic group more often than others. Which Responsible AI principle is most directly impacted?

Answer: Fairness

Explanation: Even if a model is accurate, consistently disadvantaging a specific group represents a fairness issue.

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Question 3

Which factor is a common source of unfair outcomes in AI systems?

Answer: Biased or unrepresentative training data

Explanation: If training data reflects historical or societal bias, the AI model may learn and reproduce those unfair patterns.

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Question 4

Which AI workload is most likely to raise fairness concerns?

Answer: All AI workloads that impact people

Explanation: Fairness applies to machine learning, computer vision, NLP, and generative AI workloads whenever decisions or outputs affect individuals or groups.

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Question 5

A facial recognition system performs well for some skin tones but poorly for others. What is the primary concern?

Answer: Unfair performance across different groups

Explanation: Unequal accuracy across populations indicates a fairness issue, even if average performance is high.

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Question 6

Which action helps assess fairness in an AI solution?

Answer: Comparing model outcomes across different demographic groups

Explanation: Fairness must be measured by evaluating how results differ between groups, not assumed by default.

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Question 7

Which statement about fairness and accuracy is true?

Answer: A highly accurate AI model can still be unfair

Explanation: Accuracy measures correctness overall, while fairness measures equitable treatment across groups.

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Question 8

Why must fairness be monitored after an AI solution is deployed?

Answer: Because data and real-world conditions can change over time

Explanation: New data patterns can introduce bias, making ongoing monitoring essential to maintain fairness.

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Question 9

Which Microsoft concept groups fairness with principles such as transparency and accountability?

Answer: Responsible AI

Explanation: Fairness is one of Microsoft’s six Responsible AI principles that guide the design and use of AI solutions.

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Question 10

An organization wants to ensure its AI system does not reinforce existing social inequalities. Which principle should guide this effort?

Answer: Fairness

Explanation: The goal of fairness is to prevent AI systems from amplifying historical or societal biases and inequalities.

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Exam tip

For AI-900, focus on recognizing fairness issues in scenarios rather than technical mitigation techniques. If a question describes unequal treatment of people or groups, fairness is almost always the correct principle to consider.

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