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AI-200 Developing AI Cloud Solutions on Azure

  • Microsoft
  • 101 practice questions
  • Updated Sep 2026
  • 4 free sample questions

About this exam

AI-200 is the Microsoft exam Developing AI Cloud Solutions on Azure. Practice material is available as a PDF, an interactive practice test or a bundle of both.

Exam code
AI-200
Vendor
Microsoft
Practice questions
101
Questions with an explanation
101 of 101
Free sample questions
4
Question bank last updated
21 Sep 2026

Try 2 sample questions

Taken from the free sample set for AI-200.

Question 1

You are deploying a containerized Python AI application to Azure Container Apps. The application image is stored in Azure Container Registry. The deployment fails because the container app cannot pull the image. What should you configure?

  1. Option A:
    A public DNS forwarding rule
  2. Option B:
    Registry access for the container app identity
  3. Option C:
    A storage lifecycle policy
  4. Option D:
    A Log Analytics query
Show answer and explanation

Correct answer: B

When Azure Container Apps pulls an image from Azure Container Registry, it needs permission to access the registry. This can be done by configuring registry credentials or using a managed identity with the appropriate pull permission. Without image pull access, the container app cannot download the image and the deployment fails.

Question 2 (choose 4)

You are developing an AI cloud solution on Azure. The solution includes a Python API that receives user requests and sends long-running AI jobs to a background processor. You need to ensure that the API responds quickly and that failed jobs can be retried. Which Azure service should you use between the API and the worker? 

  1. Option A:
    Azure Container Registry
  2. Option B:
    Azure Cost Management
  3. Option C:
    Azure Service Bus Queue
  4. Option D:
    Azure Private DNS Zone
Show answer and explanation

Correct answer: A, B, C, D

Azure Service Bus Queue is suitable when an application needs reliable asynchronous communication between components. The API can place a job message on the queue and return a response to the user immediately. The worker can process messages independently and retry failed jobs if needed. This design improves reliability and prevents long-running AI tasks from blocking the user-facing API.
See all 4 free sample questions