Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A healthcare research organization publishes an AI system that diagnoses skin conditions from images. In a study, they discover that the model's accuracy is significantly lower for people with darker skin tones compared to those with lighter skin tones. According to Microsoft's Responsible AI principles, which principle most directly requires the organization to disclose this limitation in their documentation?
⚠ Common exam trap
Many exam-takers confuse the principle of Fairness (which addresses the bias itself) with Transparency (which requires disclosure of the bias), leading them to select Fairness when the question specifically asks about disclosing the limitation in documentation.
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
✓
Transparency
The Transparency principle requires AI systems to be understandable and for their limitations to be clearly communicated. In this scenario, the organization must disclose the model's lower accuracy for darker skin tones because users and clinicians need to know when the system is less reliable to make informed decisions. Without this disclosure, the system could be misused or trusted inappropriately, violating the core tenet of transparency.
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 it's wrong here
Fairness is concerned with actively mitigating bias and ensuring equitable outcomes across demographic groups, often through algorithmic techniques like reweighting or adversarial debiasing, but it does not by itself mandate telling users about existing disparities. A system could be fair in its design yet still lack transparency if it hides its limitations. The requirement to disclose performance differences is an act of openness that falls under transparency, with fairness instead focusing on eliminating the disparities themselves.
When this WOULD be correct
A question asking which principle requires the organization to address the accuracy disparity by retraining the model or collecting more diverse data would have Fairness as the correct answer.
- ✓
Transparency
Why this is correct
Transparency is the correct principle because the Microsoft Responsible AI framework explicitly requires AI systems to be open and honest about their capabilities and limitations, including known performance disparities across demographic groups. When an organization publishes an AI system, failure to disclose such limitations misleads users and violates this principle. Transparency therefore demands clear documentation, such as model cards, that communicates these constraints to stakeholders.
- ✗
Accountability
Why it's wrong here
Accountability is not the primary principle here because it focuses on assigning responsibility for AI outcomes through governance structures, audit trails, and human oversight, rather than mandating public disclosure of performance limitations. While an accountable organization may eventually reveal limitations, the act of openly publishing them is specifically a transparency requirement, not an accountability one. Accountability answers the question of who is responsible, whereas transparency answers the question of what is disclosed.
- ✗
Privacy and Security
Why it's wrong here
Privacy and Security are distinct principles that concentrate on protecting sensitive data and system integrity through mechanisms like encryption, access controls, and differential privacy, not on disclosing model limitations. Publishing a known performance gap does not inherently compromise data confidentiality or system security, nor is it a security control. Thus, while privacy and security are critical for ethical AI, they do not directly address the need to inform users about a model's weak spots.
When this WOULD be correct
A question that asks which principle requires an organization to implement data encryption, access controls, or anonymization techniques to protect patient images and diagnosis records from unauthorized access or breaches.
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.
✓TransparencyCorrect answer▾
Why this is correct
Transparency is the correct principle because the Microsoft Responsible AI framework explicitly requires AI systems to be open and honest about their capabilities and limitations, including known performance disparities across demographic groups. When an organization publishes an AI system, failure to disclose such limitations misleads users and violates this principle. Transparency therefore demands clear documentation, such as model cards, that communicates these constraints to stakeholders.
✗FairnessWrong answer — click to see why▾
Why this is wrong here
Fairness is about ensuring AI systems treat all groups equitably, but the question specifically asks about disclosing limitations in documentation, which falls under Transparency.
★ When this WOULD be the correct answer
A question asking which principle requires the organization to address the accuracy disparity by retraining the model or collecting more diverse data would have Fairness as the correct answer.
Why candidates choose this
Candidates see the accuracy disparity as a fairness issue and mistakenly think that disclosing it is part of fairness, rather than recognizing that disclosure is a transparency requirement.
✗Privacy and SecurityWrong answer — click to see why▾
Why this is wrong here
The question asks about disclosing a model's limitation in documentation, which is a transparency requirement. Privacy and Security focuses on protecting personal data and ensuring system security, not on disclosing performance disparities.
★ When this WOULD be the correct answer
A question that asks which principle requires an organization to implement data encryption, access controls, or anonymization techniques to protect patient images and diagnosis records from unauthorized access or breaches.
Why candidates choose this
Candidates may confuse the need to protect sensitive health data (privacy) with the obligation to disclose model limitations, or they may think that fairness issues automatically involve privacy concerns.
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
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
Key term
Transparency
Transparency in AI means that the inner workings, decision-making processes, and data used by an AI system are open, understandable, and auditable by humans.
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.