AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A healthcare organization deploys an AI diagnostic system that was trained primarily on data from patients in one geographic region. When used in other regions with different demographics, the system shows significantly lower accuracy for those populations. Which Microsoft responsible AI principle is most directly violated?
⚠ Common exam trap
Watch out — candidates often confuse Fairness with Inclusiveness, but Fairness specifically addresses equitable outcomes and bias mitigation, while Inclusiveness is about designing for diverse user needs and accessibility.
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
✓
Fairness
The system's accuracy drop across different demographics directly violates the Fairness principle, which requires AI systems to treat all groups equitably and avoid bias. Because the training data was geographically homogeneous, the model learned patterns that do not generalize, leading to disparate performance for underrepresented populations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Transparency
Why it's wrong here
Transparency refers to making the workings of AI systems understandable and open; the issue here is not about explainability but about disparate performance across groups.
- ✓
Fairness
Why this is correct
Fairness requires that AI systems avoid bias and perform consistently across different demographic groups, which is directly violated by the unequal accuracy.
- ✗
Privacy
Why it's wrong here
Privacy concerns data protection and consent; the scenario does not involve misuse of personal data.
- ✗
Inclusiveness
Why it's wrong here
Inclusiveness is about designing for all users, but fairness is the principle that specifically addresses biased outcomes based on demographics.
Go deeper
Related to this question
Learn chapter
Responsible AI Principles
Key term
Responsible AI
A framework of ethical principles and practices that ensure artificial intelligence systems are developed and deployed in a transparent, fair, accountable, and safe manner.
Key term
Bias
Bias in AI is a systematic error in data or algorithms that leads to unfair or inaccurate outcomes, often reflecting real-world prejudices.
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Same concept, more angles
1 more way 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 hospital deploys an AI system that predicts patient readmission risk within 30 days of discharge. The model uses features such as age, medical history, and treatment plans. The hospital discovers that the model has a significantly higher false positive rate for patients of a certain ethnic group compared to others, even though the model's overall accuracy is similar across groups. This disparity was not intentional. Which Microsoft responsible AI principle is most directly compromised?
hard- ✓ A.Fairness
- B.Reliability and safety
- C.Privacy and security
- D.Accountability
Why A: The Fairness principle requires AI systems to treat all groups equitably and avoid discrimination. A higher false positive rate for one ethnic group, even if unintentional, represents an unfair disparity. While Inclusiveness relates to designing for all people, Fairness specifically addresses equitable outcomes and bias mitigation, so it is the most directly compromised principle in this case.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.