- A
A. Reliability and Safety
Why wrong: While the system is less reliable for darker skin tones, the primary violation is that it fails to serve those users adequately, which is a matter of inclusiveness. Reliability and Safety focuses on overall dependability and harm prevention.
- B
B. Inclusiveness
Inclusiveness requires AI systems to serve diverse populations. The model's poor performance for darker skin tones excludes those users from accurate diagnosis, directly violating this principle.
- C
C. Privacy and Security
Why wrong: Privacy and Security concern protecting personal data and preventing unauthorized access. There is no indication of a data breach or privacy violation in this scenario.
- D
D. Transparency
Why wrong: Transparency is about being open about how AI works and its limitations. While disclosing the limitation could mitigate harm, it does not fix the underlying exclusionary performance.
Quick Answer
The answer is the inclusiveness principle. This is the correct choice because the AI system was trained on a non-representative dataset from North American clinics featuring mostly lighter skin tones, causing it to fail for darker skin tones—a direct violation of the Microsoft responsible AI inclusiveness principle example, which demands that AI systems perform equitably across all demographic groups. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of how biased training data leads to unfair outcomes, often appearing as a trap where test-takers might confuse inclusiveness with reliability or fairness. A common memory tip is to think of the word “inclusive” as meaning “everyone included”—if the model works poorly for darker skin tones, it has excluded a group, breaking this principle.
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 startup develops an AI system that uses images of skin lesions to diagnose skin cancer. The model is trained exclusively on images from dermatology clinics in North America, which primarily feature lighter skin tones. When the system is deployed globally via a mobile app, it shows high accuracy for lighter skin tones but significantly lower accuracy for darker skin tones. Which Microsoft responsible AI principle is most directly violated?
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
B. Inclusiveness
The correct answer is B. Inclusiveness. The model was trained exclusively on images from North American dermatology clinics, which primarily feature lighter skin tones, leading to significantly lower accuracy for darker skin tones. This directly violates the inclusiveness principle, which requires AI systems to be designed for and perform well across all user groups, regardless of skin tone or other demographic characteristics.
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.
- ✗
A. Reliability and Safety
Why it's wrong here
While the system is less reliable for darker skin tones, the primary violation is that it fails to serve those users adequately, which is a matter of inclusiveness. Reliability and Safety focuses on overall dependability and harm prevention.
- ✓
B. Inclusiveness
Why this is correct
Inclusiveness requires AI systems to serve diverse populations. The model's poor performance for darker skin tones excludes those users from accurate diagnosis, directly violating this principle.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
C. Privacy and Security
Why it's wrong here
Privacy and Security concern protecting personal data and preventing unauthorized access. There is no indication of a data breach or privacy violation in this scenario.
- ✗
D. Transparency
Why it's wrong here
Transparency is about being open about how AI works and its limitations. While disclosing the limitation could mitigate harm, it does not fix the underlying exclusionary performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse inclusiveness with reliability, thinking that lower accuracy for some groups is a reliability issue, but the principle of inclusiveness specifically addresses fairness and performance across all user groups, not just system uptime or error rates in general.
Trap categories for this question
Scenario analysis trap
Privacy and Security concern protecting personal data and preventing unauthorized access. There is no indication of a data breach or privacy violation in this scenario.
Detailed technical explanation
How to think about this question
Under the hood, this is a classic case of dataset bias where the training distribution does not represent the real-world deployment distribution. In medical imaging, skin lesion datasets often have a high proportion of lighter skin tones, leading to models that learn features correlated with skin tone rather than lesion pathology. Real-world scenarios, such as the deployment of a dermatology AI app in regions with diverse populations, can result in misdiagnosis for underrepresented groups, highlighting the need for stratified data collection and fairness-aware machine learning techniques.
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: B. Inclusiveness — The correct answer is B. Inclusiveness. The model was trained exclusively on images from North American dermatology clinics, which primarily feature lighter skin tones, leading to significantly lower accuracy for darker skin tones. This directly violates the inclusiveness principle, which requires AI systems to be designed for and perform well across all user groups, regardless of skin tone or other demographic characteristics.
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
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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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