AI-102 exam dumps

AI-102 practice question 52 of 493

Designing and Implementing a Microsoft Azure AI Solution. Professional level, Microsoft. Free question with the correct answer and a full explanation.

AI-102 Question 52

Single answer

You are tasked with deploying a pre-trained machine learning model packaged as a Docker container to Azure. The model must be highly available and scalable, and you want to minimize the operational overhead of managing the infrastructure. Which Azure service should you choose to host the container?

  1. A

    Azure Kubernetes Service (AKS)

  2. B

    Azure Container Instances (ACI)

  3. C

    Azure App Service with Docker containers

  4. D

    Azure Functions with container support

Show answer and explanation

Correct answer: A

Explanation

The correct answer is Azure Kubernetes Service (AKS) because it provides a fully managed Kubernetes environment for deploying, scaling, and managing containerized applications. It is well-suited for scenarios that require high availability, scalability, and minimal infrastructure management. While other options like ACI and Azure App Service can host containers, they do not provide the same level of orchestration and scalability features as AKS.

  • A. Correct.

    Azure Kubernetes Service (AKS) is the best choice for hosting containers requiring high availability and scalability while minimizing infrastructure management overhead. AKS handles orchestration and scaling of containers efficiently.

  • B. Incorrect.

    Azure Container Instances (ACI) is suitable for single-container deployments or lightweight scenarios but lacks advanced orchestration capabilities needed for high availability and scalability.

  • C. Incorrect.

    Azure App Service with Docker containers is primarily designed for hosting web applications and may not be the optimal choice for hosting a general-purpose machine learning model container with complex scalability needs.

  • D. Incorrect.

    Azure Functions with container support is designed for event-driven, serverless workloads and not for hosting long-running, scalable, stateful containerized applications like a machine learning model.

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