AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A hospital is deploying an AI system that recommends treatment plans based on patient data. The chief medical officer insists that doctors must be able to understand why the AI recommended a specific treatment. Which Microsoft responsible AI principle is most directly relevant to this requirement?
⚠ Common exam trap
Many candidates confuse transparency with accountability, thinking that assigning blame or ownership for the AI's output satisfies the need for explanation, but transparency specifically requires the system to be interpretable and explainable, not just governed.
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 by humans. In this scenario, the chief medical officer's demand that doctors must understand why the AI recommended a specific treatment directly aligns with transparency, which includes providing explanations for model outputs, such as feature importance or decision paths, to enable clinical validation and trust.
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
Reliability and safety in the Microsoft responsible AI framework focuses on systems operating consistently, avoiding failures, and remaining robust under a wide range of conditions. While critical, this principle is about whether the AI performs correctly and safely, not about making the internal logic or rationale behind a specific treatment recommendation understandable to a clinician. The doctor's need to know 'why' is fundamentally an explainability requirement, which reliability alone does not satisfy.
- ✗
Fairness
Why it's wrong here
Fairness is concerned with ensuring AI systems treat all patient groups equitably, mitigating biases related to race, gender, age, or socioeconomic status. It addresses systematic, group-level disparities in outcomes, not the individualized reasoning for a single treatment decision. Understanding why one specific recommendation was made for one patient is a transparency issue; fairness mechanisms like bias metrics and disparity audits do not inherently expose the causal chain for individual predictions.
- ✓
Transparency
Why this is correct
Transparency in AI requires that systems be explainable, interpretable, and open to inspection, enabling users to trace how inputs led to outputs. For a doctor, transparency means being able to see the rationale behind a treatment suggestion—such as which clinical features were weighted most heavily—so they can validate or challenge the recommendation. This principle directly aligns with the clinician's need to understand why the AI gave a particular treatment, making it the correct answer.
- ✗
Accountability
Why it's wrong here
Accountability ensures that clear human ownership and responsibility exist for AI outcomes, typically through governance, roles like a responsible AI lead, and audit trails. It answers 'who is responsible' when the system makes a decision, but it does not require that the decision-making process itself be comprehensible to the end user. A doctor could know exactly who to hold accountable for a flawed recommendation and still not understand why the AI proposed it, so accountability falls short of the stated need.
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
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
About these practice questions
Courseiva writes every AI-900 question from scratch — 985 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.