Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A bank deploys an AI system to approve loan applications. The system was trained on historical data that contains systematic biases against certain ethnic groups. Despite awareness of this bias, the bank proceeds with deployment, expecting the system to correct itself over time. Which Microsoft responsible AI principle is most directly violated?
⚠ Common exam trap
Microsoft often tests the distinction between Fairness and Transparency—candidates may confuse 'knowing about bias' (transparency) with 'acting on bias' (fairness), but the core violation here is the failure to ensure equitable treatment, not the lack of disclosure.
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 bank knowingly deployed an AI system trained on biased historical data, expecting it to self-correct. This directly violates the Fairness principle, which requires AI systems to treat all groups equitably and avoid discrimination. Microsoft's responsible AI framework mandates that biases be actively identified and mitigated before deployment, not left to chance.
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
A loan approval AI must treat applicants equitably across all demographic groups. If historical loan data encodes past discrimination, the model can learn and systematically propagate those disparities, which violation is the core of the fairness principle. This is the primary ethical failure in the scenario because the model's decisions themselves are biased, not merely unexplained or lacking transparency.
- ✗
Reliability and safety
Why it's wrong here
Reliability and safety focus on whether a system performs consistently as designed and avoids catastrophic harms like crashes or unsafe recommendations. A biased loan model might be highly reliable—providing the same unfair result every time—and does not inherently introduce physical or operational safety hazards. Although fairness failures can cause broad social harm, they are not captured by reliability and safety's performance- and safety-oriented definition.
When this WOULD be correct
A medical AI system misdiagnoses patients due to insufficient training data, causing harm. The question asks which principle is violated when the system is unreliable and unsafe, making 'Reliability and safety' the correct answer.
- ✗
Transparency
Why it's wrong here
Transparency concerns whether stakeholders can understand how a model reached a decision, such as which features drove an approval or rejection. However, a perfectly transparent system could still be discriminatory if features are correlated with race or gender, so the absence of explainability is not the main issue. The bank's problem is the biased outcome, not the model's opacity, making transparency the wrong selection.
When this WOULD be correct
A healthcare AI system diagnoses patients but does not provide any explanation for its decisions, and patients cannot understand why a diagnosis was made. This would violate the transparency principle because the system lacks explainability.
- ✗
Privacy and security
Why it's wrong here
Privacy and security govern the collection, storage, and protection of personal data, including encryption, access controls, and consent compliance. The bias in loan approvals arises from how features are weighted or how training data is biased, not from mishandling applicant data; even a system with robust privacy protections can still deny loans unfairly. Thus, this option addresses data stewardship rather than the fairness violation, so it is incorrect.
When this WOULD be correct
A healthcare organization deploys an AI system that processes patient medical records without obtaining proper consent or implementing encryption, leading to a data breach. In this scenario, the Privacy and security principle is most directly violated.
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
A loan approval AI must treat applicants equitably across all demographic groups. If historical loan data encodes past discrimination, the model can learn and systematically propagate those disparities, which violation is the core of the fairness principle. This is the primary ethical failure in the scenario because the model's decisions themselves are biased, not merely unexplained or lacking transparency.
✗Reliability and safetyWrong answer — click to see why▾
Why this is wrong here
The question explicitly describes a bias against ethnic groups, which directly violates the Fairness principle. Reliability and safety concerns system failures or incorrect predictions, not discriminatory outcomes.
★ When this WOULD be the correct answer
A medical AI system misdiagnoses patients due to insufficient training data, causing harm. The question asks which principle is violated when the system is unreliable and unsafe, making 'Reliability and safety' the correct answer.
Why candidates choose this
Candidates may think that biased decisions make the system unreliable or unsafe, conflating fairness with reliability, but the core issue here is discrimination, not system dependability.
✗TransparencyWrong answer — click to see why▾
Why this is wrong here
The question describes a bias against ethnic groups, which directly violates the fairness principle. Transparency concerns openness about system behavior, but the core violation here is the lack of fairness due to biased outcomes.
★ When this WOULD be the correct answer
A healthcare AI system diagnoses patients but does not provide any explanation for its decisions, and patients cannot understand why a diagnosis was made. This would violate the transparency principle because the system lacks explainability.
Why candidates choose this
Candidates may confuse 'transparency' with 'fairness' because both involve ethical concerns, and they might think that not disclosing the bias is a transparency issue rather than recognizing that the bias itself is a fairness violation.
✗Privacy and securityWrong answer — click to see why▾
Why this is wrong here
The question focuses on bias against ethnic groups, which directly violates the Fairness principle. Privacy and security are not relevant because the issue is not about unauthorized data access or misuse of personal information.
★ When this WOULD be the correct answer
A healthcare organization deploys an AI system that processes patient medical records without obtaining proper consent or implementing encryption, leading to a data breach. In this scenario, the Privacy and security principle is most directly violated.
Why candidates choose this
Candidates may confuse bias-related issues with privacy concerns, thinking that biased data involves mishandling of personal information, or they may not clearly distinguish between fairness and privacy principles.
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
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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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.