Struggling with on-premises Azure AI integration? Learn step-by-step container deployment strategies for sensitive data compliance (Docker/Kubernetes) and master hybrid AI architectures for the AI-102 exam. Boost your Azure AI Engineer certification success with practical security best practices.
Table of Contents
Question
Your organization, Nutex Inc., operates an on-premises environment and hosts application servers on-site. Your application processes sensitive data stored in an on-premises SQL Server database. For compliance and security reasons, you want to ensure that this sensitive data remains within your local network. You plan to use Azure AI services to enhance your application’s capabilities.
What should you do to integrate Azure AI services while ensuring sensitive data remains on-premises?
A. Deploy the Azure AI services directly to the cloud, and use a secure VPN to access the data on-premises.
B. Deploy Azure AI services using containers on your on-premises servers using Docker or Kubernetes.
C. Use Azure App Service to deploy the AI services container, and configure it to access the on-premises SQL Server.
D. Deploy Azure AI services in a container using Azure Arc, extending Azure management and security to on-premises environments.
Answer
B. Deploy Azure AI services using containers on your on-premises servers using Docker or Kubernetes.
Explanation
In the given scenario, you would deploy Azure AI services using containers on your on-premises servers using Docker or Kubernetes. This ensures that all data processing remains within your local network, fully complying with the security and compliance requirements. This method provides the flexibility and control needed to manage and deploy AI services without transferring any data to the cloud. By using containers, you can easily manage, scale, and update your AI services while ensuring that sensitive data stays on-premises.
You would not deploy the Azure AI services directly to the cloud, and use a secure VPN to access the data on-premises in the given scenario. This approach does not meet the requirement of keeping data strictly on-premises. Additionally, there could be performance latency issues and increased security risks associated with data transfer over the network.
You would not use Azure App Service to deploy the AI services container, and configure it to access the on-premises SQL Server in the given scenario. Azure App Service is primarily designed for hosting web applications and APIs in the cloud. Although it can access on-premises resources via hybrid connections or VPNs, this method still involves sending data to and from the cloud, which does not ensure that the sensitive data remains entirely on-premises.
You would not deploy Azure AI services in a container using Azure Arc, extending Azure management and security to on-premises environments in the given scenario. Azure Arc extends Azure management capabilities to on-premises and multi-cloud environments, allowing you to manage resources consistently across environments. While it can manage Kubernetes clusters and AI services, the primary control and deployment still rely on the cloud. This option might involve some data being processed or managed through Azure, which does not guarantee that all sensitive data remains strictly on-premises.
Microsoft Azure AI Engineer Associate AI-102 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Engineer Associate AI-102 exam and earn Microsoft Azure AI Engineer Associate AI-102 certification.