Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A hospital uses an AI system to analyze patient records and provide treatment recommendations. They want to ensure that individual patients cannot be re-identified from the data used to train the model. Which Microsoft responsible AI principle is most directly relevant to this requirement?
⚠ Common exam trap
A common mix-up: candidates confuse the Privacy and Security principle with Fairness, mistakenly thinking that preventing re-identification is about ensuring equal treatment rather than protecting personal data from exposure.
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 requirement to prevent re-identification of individual patients from training data directly aligns with the Privacy and Security principle. This principle mandates that data be anonymized or de-identified to protect personal information, ensuring that individuals cannot be traced back from the dataset. In AI systems, this involves techniques like differential privacy, which adds noise to data to obscure individual contributions while preserving overall statistical patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Fairness
Why it's wrong here
Fairness in AI is concerned with mitigating bias and ensuring equitable treatment across demographic groups such as race, gender, or age. While analyzing patient records could raise fairness considerations, the stated requirement to prevent re-identification is fundamentally a data-protection concern, not a bias or discrimination concern. Thus, fairness is not the principle that directly addresses this need.
- ✓
Privacy and security
Why this is correct
Privacy and security is the Responsible AI principle that mandates safeguarding sensitive data, controlling access, and preventing re-identification of individuals. In a healthcare context, patient records are protected by regulations like HIPAA, and compliance requires implementing robust authentication, encryption, and de-identification techniques. This exactly matches the hospital's requirement to analyze records without exposing patients' identities.
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Inclusiveness
Why it's wrong here
Inclusiveness centers on designing AI systems that empower and serve all people equitably, including those with disabilities or from underrepresented communities. It involves considering accessibility in user experience, training data, and deployment, rather than addressing re-identification or data confidentiality. The hospital's goal of preventing re-identification is about data handling, not about making the system universally accessible.
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Accountability
Why it's wrong here
Accountability is the principle that organizations and individuals must own the outcomes of their AI systems through clear governance, audit trails, and human oversight. It ensures responsibility for decisions and compliance but does not inherently cover technical safeguards like re-identification prevention. Therefore, while a hospital must be accountable for privacy compliance, that is a governance layer distinct from the privacy and security principle.
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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
Privacy and security
Privacy and security refer to the practices and technologies used to protect sensitive data from unauthorized access while ensuring individuals' rights over their personal information are respected.
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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.