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AI Security and Compliance in Azure Flashcards
Microsoft Azure AI Fundamentals AI-900 Flashcards
Study our AI Security and Compliance in Azure flashcards for the Microsoft Azure AI Fundamentals AI-900 exam with 20+ flashcards. View as flashcards, a searchable table, or as a fun matching game.
| Front | Back |
| Define Confidential Computing in Azure. | Protects data in use by executing workloads in secure enclaves on Azure Confidential VMs |
| Describe data minimization for AI solutions. | Collect and process only the data necessary for the AI model reducing privacy risks |
| Explain pseudonymization vs anonymization. | Pseudonymization replaces identifiers with pseudonyms while anonymization irreversibly removes identifiers |
| How can you ensure data residency requirements for AI workloads on Azure? | Deploy resources in specific Azure regions that comply with local data residency laws |
| How can you secure AI model endpoints over the network in Azure? | Use Azure Private Link or deploy endpoints inside an Azure Virtual Network |
| How do Azure Blueprints help in AI compliance? | Provide repeatable templates of Azure resource deployments with built in compliance settings |
| How do managed identities enhance security for Azure AI services? | Provide Azure AD identities for services eliminating the need for credential management |
| How do you audit AI deployments on Azure? | Use Azure Monitor Azure Activity Logs and Azure Audit Logs for tracking changes and access |
| How is data encrypted in transit for Azure AI services? | Transport Layer Security TLS ensures encryption between clients and Azure services |
| Name a CI/CD security practice for MLOps in Azure. | Implement secure pipelines with GitHub Actions Azure DevOps and integrate security scanning of models and containers |
| Name a service for unified data governance and cataloging in Azure. | Azure Purview |
| What Azure feature helps classify and label sensitive data in AI solutions? | Azure Information Protection |
| What encryption options does Azure offer for data at rest in AI workloads? | Azure Storage encryption with Microsoft managed keys Azure Key Vault customer managed keys or Azure Disk Encryption |
| What is differential privacy in the context of Azure AI? | Technique that adds noise to data to protect individual privacy while enabling aggregate analysis |
| What is the purpose of Azure AD role-based access control (RBAC) in AI solutions? | Restricts access to Azure resources by assigning roles and permissions to users groups and applications |
| What is the role of Azure Sentinel in AI security? | Cloud native SIEM for collecting analyzing and responding to security incidents |
| What principles are covered by Azure Responsible AI? | Fairness reliability safety privacy inclusiveness transparency |
| Which Azure feature enforces compliance policies across AI resources? | Azure Policy |
| Which Azure service provides real time threat detection and advanced security for AI environments? | Microsoft Defender for Cloud (formerly Azure Security Center) |
| Which compliance certifications are commonly relevant for AI in Azure? | ISO27001 SOC GDPR HIPAA |
About the Flashcards
Flashcards for the Microsoft Azure AI Fundamentals exam focus on security, privacy, and governance for Azure solutions. The set helps you review terminology, concepts, and key ideas such as role-based access control, managed identities, network protections (Private Link and virtual networks), and encryption for data at rest and in transit.
Cards also cover data governance and compliance workflows-Azure Purview, Policy, Blueprints, Defender for Cloud, Sentinel, and Information Protection-along with auditing (Monitor and Activity/Audit Logs), data residency, Confidential Computing, differential privacy and pseudonymization, secure MLOps pipelines, and responsible design principles so you can confidently recall what the exam tests.
Topics covered in this flashcard deck:
- RBAC and managed identities
- Network security (Private Link, VNet)
- Encryption: rest and transit
- Data governance and classification
- Compliance, auditing, residency
- MLOps and responsible principles