Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A healthcare organization deploys an AI system that analyzes patient genetic data to recommend personalized treatments. To ensure patient data is protected from unauthorized access during use, which Microsoft responsible AI principle is most directly relevant?
⚠ Common exam trap
Test-takers frequently confuse 'data protection' with 'fairness' or 'transparency', not realizing that unauthorized access is a security breach, not a bias or explainability issue.
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
✓
Privacy and security
The scenario explicitly focuses on protecting patient genetic data from unauthorized access, which directly falls under the Privacy and security principle. This principle ensures that data is encrypted, access-controlled, and compliant with regulations like HIPAA or GDPR, preventing breaches during AI model inference or storage.
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 not the issue here because nothing in the scenario describes differential treatment, bias, or inequitable outcomes across demographic groups. Fairness addresses whether the AI system's decisions systematically disadvantage certain populations based on attributes like race, gender, or age. Protecting genetic patient data is a data-governance concern, not a bias-mitigation concern, so this principle does not match the stated need.
- ✗
Reliability and safety
Why it's wrong here
Reliability and safety would be the focus if the question were about the model producing medically incorrect predictions, handling edge cases, or failing in ways that endanger patients during clinical use. Although an AI system in healthcare must be dependable, the scenario's emphasis is on controlling who can access the data, not on ensuring the model's outputs are accurate or fail-safe. Thus, this pillar is relevant to a different risk than the one described.
- ✗
Transparency
Why it's wrong here
Transparency concerns how openly the AI system's decisions can be understood and explained—for example, providing model cards, feature attributions, or human-readable justifications for a diagnosis. The scenario does not mention interpretability or the need to explain the system's reasoning; it focuses on safeguarding sensitive data from unauthorized access. Therefore, transparency is not the principle that addresses the patient information protection described here.
- ✓
Privacy and security
Why this is correct
Privacy and security is the correct principle because the scenario centers on protecting patients' genetic information—a highly sensitive category of personal data—from unauthorized access or disclosure. In Azure AI, this maps to implementing data encryption, identity-based access control, role-based permissions, and compliance with regulations such as HIPAA or GDPR. Without these safeguards, the system could expose personal health data even if its predictions are perfectly accurate.
Go deeper
Related to this question
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Responsible AI Principles
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.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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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.