AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A hospital uses an AI system to recommend treatment plans for patients. The system's decision process is complex and not easily understood by doctors. The hospital wants to ensure that doctors can trust and verify the system's recommendations. Which Microsoft responsible AI principle is most directly relevant?
⚠ Common exam trap
Many exam-takers confuse 'transparency' with 'reliability and safety' because both involve trust, but transparency specifically addresses understandability and verifiability of the decision process, not just system robustness.
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 scenario describes a complex AI decision process that doctors cannot easily understand, which directly relates to the need for interpretability and openness about how the system works. Transparency is the Microsoft responsible AI principle that focuses on making AI systems understandable and providing clear documentation, so users can verify and trust the outputs. By ensuring transparency, the hospital can enable doctors to audit the reasoning behind treatment recommendations, fostering trust and accountability.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reliability and safety
Why it's wrong here
While reliability and safety are essential in clinical settings, they address whether the AI system consistently performs correctly and avoids patient harm, not whether its recommendations are understandable. These principles focus on error tolerance, robustness, and fail-safe behavior, but a system can be highly reliable yet still make opaque, unexplainable treatment decisions. Therefore, they do not meet the specific need for intelligible recommendations described in the scenario.
- ✓
Transparency
Why this is correct
Transparency is the correct principle because it requires AI systems to be explainable and for users to clearly understand how and why a decision was made. In a clinical treatment-planning context, transparency means providing clinician-interpretable rationales—such as which features or evidence drove the recommendation—enabling validation and trust. This directly matches the scenario's need for understandable recommendations, making it the most appropriate responsible AI principle.
- ✗
Fairness
Why it's wrong here
Fairness is concerned with mitigating algorithmic bias and ensuring that AI outputs do not systematically disadvantage any group based on protected attributes. While important for equitable treatment plans, fairness does not inherently require that individual recommendations be interpretable or justified to a clinician. A fair model could still be a 'black box' with no clear explanation of its decisions, making fairness a distinct concern from the need for transparency described here.
- ✗
Privacy and security
Why it's wrong here
Privacy and security focus on protecting patient data through encryption, access controls, and compliance with regulations like HIPAA, ensuring that sensitive information is not exposed. These measures do not address whether the AI's treatment recommendations are explainable to a human reviewer. A system could be fully secure and yet produce opaque outputs without any rationale, so privacy and security are unrelated to the specific requirement of understandable recommendations.
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Related to this question
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Responsible AI Principles
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.
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.