Question 13 of 985
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A university deploys an AI model to predict which students are at risk of dropping out. The predictions are used to offer targeted support. Students who may be negatively impacted by this prediction have the right to understand how the model arrived at its decision. Which Microsoft responsible AI principle is most directly relevant?
⚠ Common exam trap
Microsoft often tests the distinction between transparency (explaining how a decision was made) and fairness (ensuring no bias), causing candidates to mistakenly select fairness when the question is about understanding model reasoning.
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
Transparency is the responsible AI principle that requires AI systems to be understandable and interpretable. In this scenario, students have the right to know how the model arrived at its dropout prediction, which directly aligns with transparency's goal of providing clear explanations for AI decisions. This principle ensures that affected individuals can access meaningful information about the logic and factors used by the model.
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 focuses on ensuring that AI systems do not discriminate against individuals or groups based on protected attributes such as race, gender, or age. The scenario does not mention any bias evaluation, disparate impact, or equitable treatment concerns; it is strictly about explaining why a specific prediction was made. A system could be fair in a statistical sense yet still lack individual-level explanations, so fairness is not the governing principle here.
When this WOULD be correct
A university deploys an AI model to predict student dropout risk, and it is discovered that the model systematically flags students from a particular demographic group more often than others. Which principle is most directly relevant?
- ✗
Reliability and safety
Why it's wrong here
Reliability and safety concern whether an AI system consistently performs its intended function without causing harm, such as through robust error handling and fail-safe mechanisms. The university's need to explain a prediction is about making the model's reasoning visible, not about verifying operational correctness. A perfectly reliable model could still be an opaque black box, so this principle does not address the stated requirement.
When this WOULD be correct
A hospital uses an AI system to diagnose diseases. The system must consistently produce accurate results and fail safely. Which principle ensures the system performs reliably under all conditions?
- ✓
Transparency
Why this is correct
Transparency is the responsible AI principle that requires AI systems to be understandable and the basis of their decisions to be clearly communicated to affected individuals. In this scenario, the university must tell students which factors (e.g., grades, attendance) drove the prediction, which directly aligns with transparency. This principle promotes interpretability, model documentation, and clear communication, making it the correct answer.
- ✗
Privacy and security
Why it's wrong here
Privacy and security involve safeguarding personal data from unauthorized access and ensuring the AI system is resilient against attacks, such as data breaches or adversarial inputs. While student records are sensitive, the request is for an explanation of a model's decision, not for data protection measures. Transparency can coexist with privacy (e.g., explaining without revealing raw data), but the core requirement here is about understandability, not security.
When this WOULD be correct
A healthcare organization uses an AI system to predict patient readmission risks, and patients are concerned about their medical data being exposed or used without consent. The most relevant principle would be privacy and security.
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 responsible AI principle that requires AI systems to be understandable and the basis of their decisions to be clearly communicated to affected individuals. In this scenario, the university must tell students which factors (e.g., grades, attendance) drove the prediction, which directly aligns with transparency. This principle promotes interpretability, model documentation, and clear communication, making it the correct answer.
✗FairnessWrong answer — click to see why▾
Why this is wrong here
Fairness is about ensuring AI systems treat all people equitably and avoid bias, but the question specifically asks about the right to understand how a decision was made, which is a transparency concern.
★ When this WOULD be the correct answer
A university deploys an AI model to predict student dropout risk, and it is discovered that the model systematically flags students from a particular demographic group more often than others. Which principle is most directly relevant?
Why candidates choose this
Candidates may confuse fairness with transparency because both involve ethical AI, and they might think that understanding the model's decision is related to ensuring it is fair.
✗Reliability and safetyWrong answer — click to see why▾
Why this is wrong here
The question focuses on the right to understand how a model arrived at its decision, which directly relates to transparency. Reliability and safety concern system performance and robustness, not explainability.
★ When this WOULD be the correct answer
A hospital uses an AI system to diagnose diseases. The system must consistently produce accurate results and fail safely. Which principle ensures the system performs reliably under all conditions?
Why candidates choose this
Candidates may confuse the need for the model to be 'reliable' in its predictions with the need for the model's decisions to be 'explainable,' or they may think that understanding the decision is part of ensuring reliability.
✗Privacy and securityWrong answer — click to see why▾
Why this is wrong here
The question focuses on students' right to understand how the model arrived at its decision, which directly relates to transparency, not privacy and security. Privacy and security would be relevant if the concern were about unauthorized access or misuse of student data.
★ When this WOULD be the correct answer
A healthcare organization uses an AI system to predict patient readmission risks, and patients are concerned about their medical data being exposed or used without consent. The most relevant principle would be privacy and security.
Why candidates choose this
Candidates may confuse the right to understand a model's decision (transparency) with data protection (privacy), or they may assume that any student-related AI system inherently involves 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?”
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Last reviewed: Jun 30, 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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