Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
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?
⚠ Common exam trap
Test-takers frequently confuse Fairness with Inclusiveness, but Inclusiveness is about accessibility and broad user engagement, not about preventing discriminatory bias in model outcomes.
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.
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 in responsible AI requires that systems treat all people equitably and do not create or reinforce discriminatory outcomes. A hiring screening model whose recommendations vary systematically by ethnicity—even unintentionally—is a textbook fairness violation because it produces disparate impact on protected groups. Under Microsoft's responsible AI principles, this demands bias detection, mitigation, and continuous monitoring across the model lifecycle, so the correct classification is Fairness.
- ✗
Reliability and safety
Why it's wrong here
Reliability and safety address whether an AI system performs consistently, is robust to technical failures, and avoids causing physical or psychological harm. A biased screening model could still be highly reliable in the sense of producing stable, repeatable rankings; its defect is not that it crashes or behaves unpredictably, but that its decision criteria encode discriminatory patterns. The harm is inequitable treatment, not unsafe operation, so it does not fall under this principle.
- ✗
Privacy and security
Why it's wrong here
Privacy and security focus on protecting personal data from unauthorized access, misuse, or exposure, and on ensuring a system is resilient to attacks such as adversarial manipulation or data breaches. While job applications contain sensitive personal information, the described problem is that applicants of certain ethnicities receive biased recommendations—an algorithmic discrimination issue, not a confidentiality or data-protection failure. Even if data handling were perfectly secure, the biased outputs would persist, confirming this is outside the scope of privacy and security.
Go deeper
Related to this question
Learn chapter
Responsible AI Principles
Key term
Fairness
Fairness in AI means designing and deploying machine learning models that do not produce biased outcomes against any group of people based on protected characteristics like race, gender, or age.
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.
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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.