- A
Inclusiveness
Why wrong: Inclusiveness is about ensuring AI systems are designed for and accessible to all people, including those with disabilities. While related, the specific issue here is unequal treatment based on ethnicity, which is a fairness concern.
- B
Fairness
Fairness requires that AI systems do not discriminate against individuals or groups. The system's biased recommendations based on ethnicity directly violate this principle.
- C
Reliability and safety
Why wrong: Reliability and safety focus on whether the AI system functions correctly and without causing harm. The bias is not about correctness in a technical sense but about ethical discrimination.
- D
Privacy and security
Why wrong: Privacy and security involve protecting personal data and ensuring the system is not vulnerable to attacks. The issue here is discrimination, not data protection.
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 large company deploys an AI system to screen job applications and recommend candidates for interviews. After six months, an audit reveals that the system recommends candidates from certain ethnic groups at a much lower rate than others, even when those candidates have similar qualifications. 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
Fairness
The scenario describes an AI system that produces biased outcomes against certain ethnic groups despite similar qualifications, which directly violates the Fairness principle. Fairness in responsible AI requires that systems treat all people equitably and do not discriminate based on sensitive attributes like ethnicity, race, or gender. The audit finding shows the system is not fair, as it systematically disadvantages specific groups.
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.
- ✗
Inclusiveness
Why it's wrong here
Inclusiveness is about ensuring AI systems are designed for and accessible to all people, including those with disabilities. While related, the specific issue here is unequal treatment based on ethnicity, which is a fairness concern.
- ✓
Fairness
Why this is correct
Fairness requires that AI systems do not discriminate against individuals or groups. The system's biased recommendations based on ethnicity directly violate this principle.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reliability and safety
Why it's wrong here
Reliability and safety focus on whether the AI system functions correctly and without causing harm. The bias is not about correctness in a technical sense but about ethical discrimination.
- ✗
Privacy and security
Why it's wrong here
Privacy and security involve protecting personal data and ensuring the system is not vulnerable to attacks. The issue here is discrimination, not data protection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse Fairness with Inclusiveness, but Inclusiveness is about accessibility and broad user engagement, not about preventing discriminatory bias in model outcomes.
Detailed technical explanation
How to think about this question
Under the hood, fairness violations often stem from biased training data or flawed feature engineering—for example, if historical hiring data reflects past discrimination, the model learns to replicate those patterns. In practice, Microsoft's Fairlearn toolkit can be used to detect and mitigate such disparities by evaluating metrics like demographic parity or equalized odds. A real-world scenario is Amazon's scrapped AI recruiting tool, which penalized resumes containing the word 'women's' because the training data was male-dominated.
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: Fairness — The scenario describes an AI system that produces biased outcomes against certain ethnic groups despite similar qualifications, which directly violates the Fairness principle. Fairness in responsible AI requires that systems treat all people equitably and do not discriminate based on sensitive attributes like ethnicity, race, or gender. The audit finding shows the system is not fair, as it systematically disadvantages specific groups.
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.
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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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