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Microsoft AI-200 Braindumps - in .pdf Free Demo

  • Exam Code: AI-200
  • Exam Name: Developing AI Cloud Solutions on Azure
  • Last Updated: Aug 11, 2026
  • Q & A: 93 Questions and Answers
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  • Exam Name: Developing AI Cloud Solutions on Azure
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  • Q & A: 93 Questions and Answers
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Microsoft AI-200 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Develop AI solutions using Azure data services30%- Implement vector-enabled databases
  • 1. Azure Managed Redis for caching, streaming, and vector storage
  • 2. Azure Cosmos DB for NoSQL with vector search
  • 3. Azure Database for PostgreSQL with pgvector extension
- Design and optimize data access and retrieval
  • 1. Implement hybrid search and retrieval patterns
  • 2. Indexing strategies, query optimization, and consistency models
Topic 2: Develop containerized AI solutions on Azure25%- Monitor and troubleshoot containerized workloads
  • 1. Manage configurations and secrets for containers
  • 2. Log analysis, health checks, and performance monitoring
- Implement container hosting environments
  • 1. Configure scaling, networking, and security for containers
  • 2. Azure Container Registry: store, version, manage images
  • 3. Deploy to Azure Container Apps and Azure Kubernetes Service (AKS)
Topic 3: Secure, monitor, and optimize AI solutions20%- Manage security and configuration
  • 1. Azure Key Vault for secrets, keys, and certificates
  • 2. App Configuration for dynamic settings
  • 3. Managed identities and access control
- Implement observability and reliability
  • 1. Logging, metrics, and distributed tracing
  • 2. OpenTelemetry and Azure Monitor integration
  • 3. Optimize performance, cost, and scalability
Topic 4: Integrate backend services and build event-driven architectures25%- Implement messaging and event systems
  • 1. Azure Event Grid for event-driven processing
  • 2. Azure Service Bus for reliable messaging
  • 3. Connect services and expose APIs securely
- Build serverless APIs and workflows
  • 1. Azure Functions for AI integration and processing
  • 2. Orchestrate AI pipelines and workflows

Microsoft Developing AI Cloud Solutions on Azure Sample Questions:

1. 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.


2. You are developing an AI-powered API that retrieves connection strings and API keys from Azure Key Vault.
You must configure a solution that provides the following security functionality:
- The API must authenticate to Key Vault without storing credentials in any application configuration files.
- The identity used by the API must have only the minimum permissions
necessary to read secrets.
- The configuration must minimize the blast radius if an identity or
credential is compromised.
You need to implement a secure access strategy for the API.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A) Store a secret value in Azure App Configuration.
B) Assign the Key Vault Administrator role at subscription scope.
C) Grant the Key Vault Secrets User role at vault scope.
D) Use system-assigned managed identity.


3. 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.


4. 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.
You need to deploy a batch embedding workload according to the planned application architecture.
What should you use?

A) az aks commands
B) kubectl run and create commands
C) YAML-formatted files
D) XML-formatted files


5. You are developing an Azure Function that calls external APIs by providing an access token for the API. The access token is stored in a secret named token in an Azure Key Vault named mykeyvault. You need to ensure the Azure Function can access the token. Which value should you store in the Azure Functions app configuration?

A) KeyVault:mykeyvault;Secret:token
B) @Microsoft.KeyVault(SecretUri=https://mykeyvault.vault.azure.net/secrets/token/)
C) AZUREKVCONNSTR_ https://mykeyvault.vault.azure.net/secrets/token/
D) App:Settings:Secret:mykeyvault:token


Solutions:

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

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