Learn with Microsoft : AI-200 training material for 100% pass

Last Updated: Aug 11, 2026

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Microsoft AI-200 Exam Syllabus Topics:

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

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

1. Drag and Drop Question
You are developing a serverless Java application on Azure. You create a new Azure Key Vault to work with secrets from a new Azure Functions application.
The application must meet the following requirements:
- Reference the Azure Key Vault without requiring any changes to the
Java code.
- Dynamically add and remove instances of the Azure Functions host
based on the number of incoming application events.
- Ensure that instances are perpetually warm to avoid any cold starts.
- Connect to a VNet.
- Authentication to the Azure Key Vault instance must be removed if the Azure Functions application is deleted.
You need to grant the Azure Functions application access to the Azure Key Vault.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.


2. You store embeddings in Redis by using keys formatted as doc:(id). Some embeddings are accessed frequently. Others are rarely used.
You need to implement a caching strategy that keeps only frequently accessed embeddings in memory.
What should you use?

A) EXPIRE command
B) time-window expiration
C) volatile-ttl
D) allkeys-lru


3. 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 implement trace correlation according to the business requirements.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.


4. Your application must classify uploaded product images into one of 40 custom categories specific to your business (e.g., proprietary part numbers). What should you use?

A) Azure AI Face API
B) Azure AI Custom Vision (image classification)
C) Azure AI Document Intelligence
D) Azure AI Vision prebuilt image analysis


5. Hotspot Question
You are developing an event-driven system that reacts to new or updated documents in Azure Cosmos DB for NoSQL.
The application must process new or updated documents as they appear in the container by using a change feed processor. The application must also maintain processor state between runs.
You need to configure the components required for stateful change feed processing.
Which components should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.


Solutions:

Question # 1
Answer: Only visible for members
Question # 2
Answer: D
Question # 3
Answer: Only visible for members
Question # 4
Answer: B
Question # 5
Answer: Only visible for members

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