AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
A hospital uses an AI system to analyze patient health records for research. The hospital must ensure that all patient data is stored securely and only authorized personnel can access it. Which Microsoft responsible AI principle is most directly relevant?
⚠ Common exam trap
Many candidates confuse 'privacy and security' with 'transparency' because both involve data handling, but transparency is about model explainability, not data protection.
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 secure storage and access control of patient data, which directly aligns with Microsoft's responsible AI principle of Privacy and security. This principle ensures that data is protected against unauthorized access and breaches, often implemented through encryption (e.g., AES-256 for data at rest, TLS 1.2+ for data in transit) and role-based access control (RBAC) in Azure services like Azure SQL Database or Azure Blob 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 a Microsoft responsible AI principle focused on ensuring AI systems treat all demographic groups equitably and do not perpetuate bias in predictions or decisions. It is not a data-governance principle, so it offers no mechanisms for preventing unauthorized access, encrypting stored records, or enforcing role-based access controls. While fairness may be relevant when AI analyzes patient data, it does not satisfy a requirement to protect sensitive health information.
- ✗
Transparency
Why it's wrong here
Transparency is a responsible AI principle that promotes interpretability and explainability, allowing users to understand how and why an AI model produced a particular output. It is about open communication of model behavior, limitations, and decision-making processes, not about securing data or restricting access. Although a transparent diagnostic AI could help clinicians trust its recommendations, it does not provide the data protection capabilities like encryption, auditing, or access controls that a hospital's security requirement demands.
When this WOULD be correct
A bank uses an AI system to approve loans. The bank must clearly explain to customers how the AI makes decisions and what factors influence the outcome. Which Microsoft responsible AI principle is most directly relevant?
- ✓
Privacy and security
Why this is correct
Privacy and security is the Microsoft responsible AI principle that directly addresses how data is collected, stored, accessed, and protected throughout an AI system's lifecycle. It encompasses encryption at rest and in transit, identity and access management, threat monitoring, data anonymization, and compliance with healthcare regulations such as HIPAA or GDPR. For a hospital analyzing patient health records, this principle is the correct match because it ensures only authorized personnel can access sensitive data and that the data remains confidential and intact.
- ✗
Inclusiveness
Why it's wrong here
Inclusiveness is a responsible AI principle that guides AI design to empower all people, including those with disabilities or from under-represented groups, and to avoid excluding users based on language, culture, or ability. It emphasizes accessibility, usability, and equitable engagement with AI systems, not data security or privacy. While an inclusive AI should serve diverse patients, this principle does not address confidentiality, secure storage, or safeguarding patient health records against unauthorized disclosure.
When this WOULD be correct
A question that asks: 'Which Microsoft responsible AI principle ensures that AI systems are designed to be accessible and usable by people of all abilities and backgrounds?' would make inclusiveness the correct answer.
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.
✓Privacy and securityCorrect answer▾
Why this is correct
Privacy and security is the Microsoft responsible AI principle that directly addresses how data is collected, stored, accessed, and protected throughout an AI system's lifecycle. It encompasses encryption at rest and in transit, identity and access management, threat monitoring, data anonymization, and compliance with healthcare regulations such as HIPAA or GDPR. For a hospital analyzing patient health records, this principle is the correct match because it ensures only authorized personnel can access sensitive data and that the data remains confidential and intact.
✗TransparencyWrong answer — click to see why▾
Why this is wrong here
Transparency refers to the openness about AI system capabilities and limitations, not directly to data access control or security. The question specifically asks about storing patient data securely and restricting access, which falls under privacy and security.
★ When this WOULD be the correct answer
A bank uses an AI system to approve loans. The bank must clearly explain to customers how the AI makes decisions and what factors influence the outcome. Which Microsoft responsible AI principle is most directly relevant?
Why candidates choose this
Candidates may confuse transparency with security because both involve information handling, but transparency is about disclosure and explainability, not access control.
✗InclusivenessWrong answer — click to see why▾
Why this is wrong here
In this scenario, the core requirement is secure storage and access control of patient data, which directly relates to privacy and security, not inclusiveness. Inclusiveness focuses on designing AI systems that are accessible and fair to all users, regardless of background or ability.
★ When this WOULD be the correct answer
A question that asks: 'Which Microsoft responsible AI principle ensures that AI systems are designed to be accessible and usable by people of all abilities and backgrounds?' would make inclusiveness the correct answer.
Why candidates choose this
Candidates may confuse inclusiveness with privacy because both involve protecting user interests, but inclusiveness is about broad accessibility and non-discrimination, not data security.
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?”
Quick reference
Symmetric Encryption Algorithm Comparison
| Algorithm | Key Size | Block Size | Status | Notes |
|---|---|---|---|---|
| AES-128 | 128-bit | 128-bit | Current standard | NIST approved; WPA3, TLS |
| AES-256 | 256-bit | 128-bit | Current standard | Preferred for sensitive / govt data |
| 3DES | 112-bit effective | 64-bit | Deprecated (2023) | Replaced by AES |
| DES | 56-bit | 64-bit | Broken | Cracked in < 24 h; never deploy |
| ChaCha20 | 256-bit | Stream cipher | Current | TLS 1.3, WireGuard |
Go deeper
Related to this question
Learn chapter
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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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.