Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
What is 'AI privacy and security' in Microsoft's Responsible AI principles?
⚠ Common exam trap
Many exam-takers confuse general Azure security features (like encryption) or AI-for-security use cases with the specific Responsible AI principle of 'privacy and security,' which is about protecting data and models from harm, not just securing infrastructure or using AI defensively.
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
✓
Protecting personal data from AI systems and securing AI against adversarial attacks and misuse
Microsoft's Responsible AI principle of 'privacy and security' focuses on protecting individuals' personal data from being exposed or misused by AI systems, and ensuring AI models and infrastructure are resilient against adversarial attacks, data poisoning, and other security threats. Option B correctly captures this dual focus on data protection and system security, which is distinct from general Azure encryption or cybersecurity use cases.
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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Encrypting all data at rest in Azure storage used by AI workloads
Why it's wrong here
Encrypting data at rest in Azure storage is a specific technical security control that supports protecting AI workloads, but it is only one measure. The Responsible AI privacy and security principle encompasses a broader set of obligations including data minimization, informed consent, anonymization, and defending the AI model against adversarial inputs. Encryption alone does not address those core requirements.
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Protecting personal data from AI systems and securing AI against adversarial attacks and misuse
Why this is correct
Privacy in Responsible AI means enforcing data minimization, obtaining proper consent, and anonymizing personal data used in AI training and inference. Security requires protecting the AI model and its data pipeline against adversarial attacks, model inversion, and data poisoning. Together these form the privacy and security principle, a foundational pillar of Microsoft's Responsible AI framework.
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Using AI to enhance cybersecurity by detecting network intrusions
Why it's wrong here
Using AI to detect network intrusions is itself an AI system applied to security, not a description of the Responsible AI principle of privacy and security. That principle governs how AI systems protect personal data and resist adversarial manipulation, not how AI is used as a defensive tool. Hence it is a valid AI application but not the correct match for the principle.
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Ensuring employees don't share AI model weights externally without authorisation
Why it's wrong here
Restricting employee access to model weights protects proprietary intellectual property and trade secrets, which is an organizational security control. However, the Responsible AI privacy and security principle centers on the data subjects' personal information and the model's robustness against attacks, not on internal IP governance. While important, it does not capture the holistic ethical concern.
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Related to this question
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Responsible AI Principles
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.
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.
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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.