AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A hospital deploys an AI system to assist in diagnosing diseases from medical images. The system is a complex deep learning model that provides a diagnosis without any explanation. Doctors are skeptical and want to understand why the system made a particular recommendation. The hospital decides to deploy the system without providing any interpretability. Which Microsoft responsible AI principle is most directly being violated?
⚠ Common exam trap
It's easy for candidates to confuse 'transparency' with 'fairness' or 'reliability,' assuming that a lack of explanation implies bias or unsafe behavior, when the core violation is the absence of interpretability and accountability in the system's decision-making process.
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 system provides a diagnosis without any explanation of how it reached its conclusion, and the hospital decides to deploy it without interpretability. This directly violates the transparency principle, which requires AI systems to be understandable and for their decisions to be explainable to users, especially in high-stakes domains like healthcare.
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 concerns the equitable treatment of individuals or groups, requiring that the AI does not propagate or amplify biases related to race, gender, age, or socioeconomic status. While a lack of explainability can hide underlying bias, the scenario does not describe unequal performance across populations, so the direct violation is not fairness. The absence of bias alone would not satisfy the principle described here, because the core issue is a lack of insight into how decisions are made.
When this WOULD be correct
A healthcare AI system is found to give different diagnostic accuracy for different ethnic groups, and the hospital deploys it without addressing this disparity. This would violate the Fairness principle.
- ✗
Reliability & Safety
Why it's wrong here
Reliability & Safety address whether the system consistently performs as intended under expected conditions and avoids causing harm, such as misdiagnoses that lead to incorrect treatment. A diagnostic model might still produce accurate and safe predictions in many cases, yet fail the stated principle if clinicians cannot inspect its decision logic. The primary shortcoming in this scenario is not a demonstrated failure of performance or safety, but rather the absence of explainable reasoning that transparency mandates.
When this WOULD be correct
A question where an AI system makes incorrect diagnoses due to data drift or adversarial inputs, and the hospital deploys it without proper testing or monitoring, would make Reliability & Safety the correct answer.
- ✓
Transparency
Why this is correct
Transparency is the principle that AI systems should be open to inspection, with decisions that can be explained in human-understandable terms. Deploying a diagnostic model without any interpretability means clinicians cannot determine why a particular disease was suggested, violating the requirement that automated recommendations be auditable and explainable. Without this, the system's reasoning is effectively a black box, making it impossible for medical staff to validate or challenge its output.
- ✗
Inclusiveness
Why it's wrong here
Inclusiveness requires that AI solutions account for diverse user needs, including accessibility for people with disabilities, language differences, and varying levels of technical literacy. The hospital's problem centers on interpretability of the model's outputs, not on whether the system interface or workflow accommodates users with visual, hearing, motor, or cognitive impairments. Therefore, while an inclusive design might improve usability of the diagnostic tool, it would not resolve the specific concern that clinicians cannot understand the model's decision-making process.
When this WOULD be correct
A question where an AI system is designed only for native English speakers, ignoring non-native speakers or people with disabilities, and the principle violated is Inclusiveness. For example: 'A company deploys a voice assistant that only understands standard American English, excluding users with accents or speech impairments. Which principle is 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.
✓TransparencyCorrect answer▾
Why this is correct
Transparency is the principle that AI systems should be open to inspection, with decisions that can be explained in human-understandable terms. Deploying a diagnostic model without any interpretability means clinicians cannot determine why a particular disease was suggested, violating the requirement that automated recommendations be auditable and explainable. Without this, the system's reasoning is effectively a black box, making it impossible for medical staff to validate or challenge its output.
✗FairnessWrong answer — click to see why▾
Why this is wrong here
The question focuses on the lack of explanation for the AI's diagnosis, which directly violates the Transparency principle. Fairness is about bias and equitable treatment, not about providing explanations.
★ When this WOULD be the correct answer
A healthcare AI system is found to give different diagnostic accuracy for different ethnic groups, and the hospital deploys it without addressing this disparity. This would violate the Fairness principle.
Why candidates choose this
Candidates may think that providing no explanation could hide unfair biases, so they incorrectly associate the lack of interpretability with fairness issues.
✗Reliability & SafetyWrong answer — click to see why▾
Why this is wrong here
The scenario describes a lack of explanation for AI decisions, which directly violates the Transparency principle. Reliability & Safety focuses on ensuring the system operates reliably and safely, not on providing explanations.
★ When this WOULD be the correct answer
A question where an AI system makes incorrect diagnoses due to data drift or adversarial inputs, and the hospital deploys it without proper testing or monitoring, would make Reliability & Safety the correct answer.
Why candidates choose this
Candidates may confuse the need for system reliability with the need for transparency, thinking that an unexplained system is unreliable, but the core issue here is the lack of interpretability, not reliability.
✗InclusivenessWrong answer — click to see why▾
Why this is wrong here
Inclusiveness focuses on ensuring the AI system serves diverse user groups and does not exclude people based on characteristics like disability or background. The scenario describes a lack of explanation for medical diagnoses, which violates Transparency, not Inclusiveness.
★ When this WOULD be the correct answer
A question where an AI system is designed only for native English speakers, ignoring non-native speakers or people with disabilities, and the principle violated is Inclusiveness. For example: 'A company deploys a voice assistant that only understands standard American English, excluding users with accents or speech impairments. Which principle is violated?'
Why candidates choose this
Candidates may confuse 'explainability' with 'inclusiveness' because both relate to user understanding, but Inclusiveness is about accessibility and representation, not about providing explanations for decisions.
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
Deep learning
Deep learning is a subset of machine learning that uses multi-layered neural networks to automatically learn patterns from large amounts of data.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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.