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Microsoft AI-200

AI-200

Exam Code: AI-200

Exam Name: Developing AI Cloud Solutions on Azure

Updated: Aug 29, 2026

Q&A Number: 157 Q&As

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Microsoft AI-200 Exam Syllabus Topics:

SectionObjectives
Develop containerized solutions on Azure- Implement containerized applications
  • 1. Manage containerized compute environments
  • 2. Deploy AI workloads in containers
  • 3. Implement scalable hosting patterns
Develop AI solutions by using Azure data management services- Work with Azure data platforms for AI workloads
  • 1. Azure data management services
  • 2. Data integration for AI applications
  • 3. Vector databases
Connect to and consume Azure services- Integrate Azure services
  • 1. Azure messaging and eventing
  • 2. Event-driven architectures
  • 3. Azure SDKs
  • 4. Serverless integration patterns
  • 5. Third-party SDKs
Secure, monitor, troubleshoot Azure solutions- Operate AI cloud solutions
  • 1. Performance optimization
  • 2. Security and secret management
  • 3. Troubleshooting Azure solutions
  • 4. Monitoring and observability

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

Question 1

Case Study 2 - Proseware Inc.
Background
Proseware Inc. develops AI-powered knowledge management solutions for enterprise customers.
The company is modernizing its platform to support semantic search, intelligent document retrieval, and real-time partner integrations.
The engineering team uses Python and Azure SDKs. The architecture is being redesigned to support containerized microservices, vector search workloads, and serverless backend processing.
Planned Application Architecture
Microservices are containerized by using Docker.
Code for containerized microservices and Azure Function apps is developed locally but stored in a GitHub repository.
Custom images for containerized microservices are stored in Azure Container Registry (ACR).
Base images are stored in Docker Hub. Custom images must be rebuilt automatically whenever their base images are updated.
Azure Cosmos DB for NoSQL stores documents, metadata, and vector embeddings.
Azure Functions generate vector embeddings of Azure Cosmos DB for NoSQL-hosted documents and send messages to Service Bus to trigger search index updates.
Azure Container Apps (ACA) apps host backend API services that provide semantic search across Azure Cosmos DB for NoSQL documents. API services process Service Bus messages and update search indexes.
Azure Kubernetes Service (AKS) processes batch vector embedding regeneration for existing Azure Cosmos DB for NoSQL documents (whenever the embedding model is changed).
An extranet-facing containerized webhook allows business partners to submit documents to be processed by internal AI workflows for semantic search and retrieval.
Monitoring
Telemetry generated by Azure resources is sent to Azure Monitor.
A Log Analytics workspace is used to collect ACA apps logs, AKS container logs, and Azure Functions apps logs.
Monitoring of Azure Functions is currently implemented by using Azure Application Insights SDK instrumentation.
Business Requirements
Embeddings for new or updated Azure Cosmos DB for NoSQL-hosted documents must be automatically generated.
Backend API services must scale automatically during business hours.
Cold start delay of backend APIs must be minimized.
Secrets must be stored outside of container images.
Developers must be able to correlate telemetry across Azure Functions hosts and apps.
All tracing must be implemented by using OpenTelemetry SDK instrumentation.
Development efforts must be minimized.
Technical Requirements
Container images must be built automatically and validated before code updates are merged into the main branch.
Image build automation must run inside the Azure Container Registry, eliminating dependency on local developer machines and external build services.
Dependency of image builds on local developer machines must be eliminated.
Event-driven scaling in ACA must occur based on the number of pending messages in the Azure Service Bus queue.
Azure Cosmos DB for NoSQL RU consumption must be minimized.
Vector similarity search must use embeddings stored in Azure Cosmos DB for NoSQL.
The partner-facing containerized webhook service must run on Azure App Service.
Secrets must NOT be stored in container images, source control, or application configuration directly. They must be accessed securely at runtime.
All secrets must be stored centrally in Azure Key Vault and accessed at runtime through a managed identity.
Azure App Service must supply secrets at runtime without relying on external services.
Resources and workloads must be deployed by using Bicep templates through an automated, version-controlled pipeline. Local and command-line deployments must be eliminated to ensure repeatable, auditable deployments.
Known Issues
RU consumption spikes during vector similarity queries.
Drag and Drop Question
You need to configure event-driven scaling for the backend API services to meet the technical requirements.
Which settings should you use for each element? To answer, move the appropriate settings to the correct elements. You may use each setting once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.


Question 2

Drag and Drop Question
You are building a back-end pipeline that receives AI inference requests.
The pipeline must perform the following actions:
- Publish a message so that multiple independent consumers receive it.
- Process messages in first-in, first-out (FIFO) order.
- Isolate any failed messages.
You need to configure the appropriate Service Bus entities.
How should you configure the Service Bus entities? To answer, move the appropriate configurations to the correct requirements. You may use each configuration once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.


Question 3

Hotspot Question
You are creating an app that uses Event Grid to connect with other services. Your app's event data will be sent to a serverless function that checks compliance. This function is maintained by your company.
You write a new event subscription at the scope of your resource. The event must be invalidated after a specific period of time.
You need to configure Event Grid.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


Question 4

Hotspot Question
You are troubleshooting latency issues by using the requests table in Azure Monitor.
You need to calculate the average request duration grouped by cloud role name and also limit results to the past 30 minutes.
Which code elements should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


Question 5

Hotspot Question
You plan to configure an Event Grid subscription for AI inference events.
The solution must:
- Filter events by payload data.
- Retain undelivered events.
- Limit retry attempts.
You need to configure the Event Grid.
Which configurations should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


Solutions:

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