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AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations

A medical research organization uses an AI system to analyze patient health records to identify patterns in disease progression. They publish a research paper that includes tables of aggregated statistics derived from the data. Later, a researcher discovers that by combining multiple statistics, it is possible to identify individual patients. Which Microsoft responsible AI principle has been most directly compromised?

⚠ Common exam trap

Test-takers frequently confuse aggregated statistics with anonymized data, assuming that tables of averages or counts cannot reveal individuals, but re-identification attacks (e.g., via differencing or linking multiple tables) directly compromise privacy and security.

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 describes a re-identification attack, where aggregated statistics (tables) can be combined to infer individual patient identities. This directly violates the privacy and security principle, which requires that AI systems protect personal data and prevent unauthorized identification. Microsoft's responsible AI principle of privacy and security emphasizes safeguarding data through techniques like differential privacy, which was not applied here.

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 in responsible AI is concerned with ensuring outcomes and decisions do not systematically disadvantage protected groups; it is evaluated through group-level metrics such as demographic parity or equalized odds. Re-identifying individuals in a medical dataset is a privacy breach that violates confidentiality, not a fairness failure. Even a model that treats all demographic groups equitably can still expose patient identities, so this option misidentifies the ethical principle at stake.

  • Privacy and security

    Why this is correct

    This principle mandates that personal and medical data be safeguarded through controls such as encryption, access management, and robust de-identification, and it directly prohibits re-identification of individuals. The described scenario—where an AI system enables patient re-identification—is a clear violation because it breaks the promise of anonymization and exposes sensitive health information. Therefore this is the correct answer, as privacy and security specifically address the unauthorized linkage of records to real people.

  • Transparency

    Why it's wrong here

    Transparency refers to clearly communicating an AI system's capabilities, limitations, and decision-making logic to stakeholders, ensuring users understand when and how AI is used. It does not speak to whether underlying data can be re-identified; a fully transparent system could still leak patient identities. This option is wrong because the failure here is data protection, not a lack of explainability or openness about the system's behavior.

  • Accountability

    Why it's wrong here

    Accountability in responsible AI concerns establishing clear human ownership, governance structures, and audit trails for how an AI system is developed and deployed. While a loss of patient privacy might eventually lead to accountability issues, the immediate harm described is unauthorized re-identification, which is a security and privacy failure rather than an absence of role ownership. Thus this principle does not match the specific violation in the scenario.

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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.