AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A university uses an AI system to screen scholarship applications. The system was trained on historical data that mostly awarded scholarships to students from STEM majors. Consequently, the system consistently gives lower scores to equally qualified students from humanities and arts majors. Which Microsoft responsible AI principle is most directly being violated by this outcome?
⚠ Common exam trap
The trap here is that candidates might confuse fairness with transparency, thinking that if the system explains its scores it becomes fair, but fairness is about the outcome itself, not the explanation.
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 AI system's training data caused it to learn a biased pattern that systematically disadvantages humanities and arts applicants, which directly violates the fairness principle. Fairness in responsible AI requires that systems treat all groups equitably and do not perpetuate or amplify existing biases, especially when making high-stakes decisions like scholarship awards.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Fairness
Why this is correct
Fairness in AI means that the system's decisions do not disproportionately disadvantage individuals or groups based on protected characteristics (e.g., race, gender, socioeconomic status). A scholarship screening system that yields biased outcomes violates this principle because it fails to ensure equitable treatment across all applicants. This principle is foundational to responsible AI and is one of the six core Microsoft AI principles.
- ✗
Reliability and safety
Why it's wrong here
Reliability and safety address whether an AI system performs its intended functions without failure and without causing physical or financial harm. A scholarship screening system might be perfectly dependable (e.g., always returning results, no downtime) yet still be biased in its decisions. The scenario describes unfair outcomes, not system malfunctions or operating hazards, so this principle is not the primary concern.
When this WOULD be correct
This option would be correct if the AI system produced inconsistent or incorrect scores due to data quality issues, such as missing or erroneous training data, leading to unreliable predictions that could cause financial harm to students.
- ✗
Privacy and security
Why it's wrong here
Privacy and security focus on safeguarding personal data through encryption, access controls, and compliance with regulations like GDPR or FERPA. A biased screening algorithm does not necessarily misuse or expose data; it could be perfectly secure yet still discriminate. The ethical failure in this scenario is about the fairness of outcomes, not data protection, so this principle is not applicable.
When this WOULD be correct
This option would be correct if the AI system exposed students' personal information (e.g., grades, financial data) without consent or had a data breach, violating data protection laws.
- ✗
Transparency
Why it's wrong here
Transparency involves openly communicating how an AI system makes decisions, such as documenting data sources, model logic, and limitations. Even a fully transparent system—one that fully explains its reasoning—can produce biased results if the underlying data or rules are prejudiced. The issue described is the unfair treatment of applicants, not the absence of disclosure, so transparency is not the violated principle.
When this WOULD be correct
A scenario where an AI system's decision-making process is hidden or not disclosed to users, such as a loan approval system that does not provide reasons for rejection, would make Transparency the correct answer.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓FairnessCorrect answer▾
Why this is correct
Fairness in AI means that the system's decisions do not disproportionately disadvantage individuals or groups based on protected characteristics (e.g., race, gender, socioeconomic status). A scholarship screening system that yields biased outcomes violates this principle because it fails to ensure equitable treatment across all applicants. This principle is foundational to responsible AI and is one of the six core Microsoft AI principles.
✗Reliability and safetyWrong answer — click to see why▾
Why this is wrong here
The question describes a bias in scholarship scoring based on major, which violates fairness, not reliability/safety. Reliability/safety concerns system failures or harm, not discriminatory outcomes.
★ When this WOULD be the correct answer
This option would be correct if the AI system produced inconsistent or incorrect scores due to data quality issues, such as missing or erroneous training data, leading to unreliable predictions that could cause financial harm to students.
Why candidates choose this
Candidates may confuse 'unfair outcomes' with 'unreliable system' because bias can make a system seem unreliable, but reliability focuses on performance consistency and safety, not equity.
✗Privacy and securityWrong answer — click to see why▾
Why this is wrong here
The question describes bias in scholarship scores based on academic major, which is a fairness issue. Privacy and security relate to protecting personal data and preventing unauthorized access, not to biased outcomes.
★ When this WOULD be the correct answer
This option would be correct if the AI system exposed students' personal information (e.g., grades, financial data) without consent or had a data breach, violating data protection laws.
Why candidates choose this
Candidates may confuse 'bias in data' with 'privacy violations' because both involve data handling, but bias is about fairness, not data protection.
✗TransparencyWrong answer — click to see why▾
Why this is wrong here
The question describes a system producing biased outcomes against certain groups, which directly violates the Fairness principle. Transparency concerns explainability and disclosure, not the bias itself.
★ When this WOULD be the correct answer
A scenario where an AI system's decision-making process is hidden or not disclosed to users, such as a loan approval system that does not provide reasons for rejection, would make Transparency the correct answer.
Why candidates choose this
Candidates may confuse the lack of fairness with a lack of transparency, thinking that if the system were more transparent, the bias would be exposed, but the core violation is still fairness.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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
Training data
Training data is a set of examples used to teach a machine learning model how to make predictions or decisions.
About these practice questions
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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.