Welcome to the AI-200: Developing AI Cloud Solutions on Azure Exam Prep Hub!
Welcome to the one-stop hub with information for preparing for the AI-200: Developing AI Cloud Solutions on Azure certification exam. The content for this exam helps prepare you to be “responsible for contributing to all phases of implementing AI solutions on Azure, with an emphasis on back-end services and components. You’re also responsible for supporting all phases of the development lifecycle, including requirements gathering, design, development, deployment, security, and monitoring”.
Upon successful completion of the exam, you earn the Microsoft Certified: Azure AI Cloud Developer Associate certification.
This hub provides information directly here (topic-by-topic as outlined in the official study guide), links to a number of external resources, tips for preparing for the exam, practice tests, and section questions to help you prepare. Bookmark this page and use it as a guide to ensure that you are fully covering all relevant topics for the AI-200 exam and making use of as many of the resources available as possible.
Audience Profile (from Microsoft’s site)
As a candidate for this Microsoft Certification, you’re responsible for contributing to all phases of implementing AI solutions on Azure, with an emphasis on back-end services and components. You’re also responsible for supporting all phases of the development lifecycle, including requirements gathering, design, development, deployment, security, and monitoring.
You should be proficient in: - Azure SDKs and third-party SDKs used in Azure. - Azure data management services. - Azure monitoring and troubleshooting. - Azure messaging and eventing. - Vector databases. - Python programming. - Implementing containerized applications on Azure.
Skills at a glance (as specified in the official study guide)
- Develop containerized solutions on Azure (20–25%)
- Develop AI solutions by using Azure data management services (25–30%)
- Connect to and consume Azure services (20–25%)
- Secure, monitor, troubleshoot Azure solutions (20–25%)
Topic-by-Topic Exam Content
[click a topic link to access the content and practice questions for that topic]
Develop containerized solutions on Azure (20–25%)
Implement container application hosting
- Build, store, version, and manage container images by using Azure Container Registry
- Build and run images by using Azure Container Registry Tasks
- Deploy containers to Azure App Service, including configuring App Service to supply environment variables and secrets
Implement container-orchestrated solutions
- Deploy applications to Azure Container Apps, including environment configuration and revision management
- Implement event-driven scaling by using Kubernetes Event‑driven Autoscaling (KEDA) in Container Apps
- Deploy and manage applications to Azure Kubernetes Service (AKS) by using manifest files
- Monitor and troubleshoot solutions on AKS and Container Apps by inspecting logs, events, and end-to-end connectivity
Develop AI solutions by using Azure data management services (25–30%)
Develop AI solutions by using Azure Cosmos DB for NoSQL
- Connect to Azure Cosmos DB for NoSQL by using the SDK and run queries
- Optimize query performance and Request Units (RUs) consumption by using indexing policies and consistency levels
- Store and retrieve embeddings and execute vector similarity search for semantic retrieval
- Implement a change feed processor to detect and handle new or updated items
Develop AI solutions by using Azure Database for PostgreSQL
- Connect and query Azure Database for PostgreSQL by using SDKs
- Model schemas and implement indexing strategies, including designing tables and choosing appropriate data types
- Implement indexing strategies, including optimizing query latency and reducing pgvector compute overhead
- Configure compute, memory, and storage resources to support vector workloads
- Run vector similarity search, including storing embeddings, semantic retrieval, and implementing retrieval-augmented generation (RAG) patterns by using metadata filter
- Implement connection optimization to improve throughput and minimize latency
Integrate Azure Managed Redis in AI solutions
- Implement Azure Managed Redis data operations, including caching, expiration, and invalidation
- Implement vector indexing to enable similarity search
Connect to and consume Azure services (20–25%)
Develop event- and message-based AI solutions
- Queue and process back-end operations by using Azure Service Bus, including dead-letter queue handling, messages, topics, and subscriptions
- Implement event-driven workflows by using Azure Event Grid, including filters, custom events, and retries
Develop and implement Azure Functions
- Build serverless APIs, including implementing triggers and bindings
- Configure and deploy function apps
Secure, monitor, and troubleshoot Azure solutions (20–25%)
Implement secure Azure solutions
- Secure secrets by using Azure Key Vault, including rotation and retrieval
- Store and retrieve app configuration information by using Azure App Configuration
Monitor and troubleshoot Azure solutions
AI-200 Practice Exams
AI-200 Practice Exam #1 (30 questions)
AI-200 Practice Exam #2 (30 questions)
AI-200 Practice Exam #3 (30 questions)
AI-200 Practice Exam #4 (30 questions)
Important AI-200 Resources
Link to the free, comprehensive, self-paced course on Microsoft Learn:
Develop AI cloud solutions on Azure
This course has 9 learning paths:
(1) Implement container application hosting on Azure
This learning path has 2 modules:
(i) Store and manage containers in Azure Container Registry
(ii) Deploy containers to Azure App Service
(2) Deploy and manage apps on Azure Container Apps
This learning path has 3 modules:
(i) Deploy containers to Azure Container Apps
(ii) Manage containers in Azure Container Apps
(iii) Scale containers in Azure Container Apps
(3) Deploy and monitor applications on Azure Kubernetes Service
This learning path has 3 modules:
(i) Deploy applications to Azure Kubernetes Service
(ii) Configure applications on Azure Kubernetes Service
(iii) Monitor and troubleshoot applications on Azure Kubernetes Service
(4) Develop AI solutions with Azure Cosmos DB for NoSQL
This learning path has 3 modules:
(i) Build queries for Azure Cosmos DB for NoSQL
(ii) Implement vector search on Azure Cosmos DB for NoSQL
(iii) Optimize query performance for Azure Cosmos DB for NoSQL
(5) Develop AI solutions with Azure Database for PostgreSQL
This learning path has 3 modules:
(i) Build and query with Azure Database for PostgreSQL
(ii) Implement vector search with Azure Database for PostgreSQL
(iii) Optimize vector search in Azure Database for PostgreSQL
(6) Enhance AI solutions with Azure Managed Redis
This learning path has 3 modules:
(i) Implement data operations in Azure Managed Redis
(ii) Implement event messaging with Azure Managed Redis
(iii) Implement vector storage in Azure Managed Redis
(7) Integrate backend services for AI solutions
This learning path has 3 modules:
(i) Queue and process AI operations with Azure Service Bus
(ii) Develop event-driven AI workflows with Azure Event Grid
(iii) Build serverless AI backends with Azure Functions
(8) Manage application secrets and configuration for AI solutions
This learning path has 2 modules:
(i) Manage application secrets with Azure Key Vault
(ii) Manage application settings with Azure App Configuration
(9) Observe and troubleshoot apps on Azure
This learning path has 2 modules:
(i) Instrument an app with OpenTelemetry
(ii) Analyze app telemetry with logs and metrics
Link to the certification page:
Link to the “Microsoft Certified: Azure AI Cloud Developer Associate” certification page.
Link to the study guide:
Link to the Study Guide for AI-200: Building Intelligent Applications.
A highly rated course on Udemy:
AI-200: Azure AI Cloud Developer Associate Exam Prep
YouTube Video Series
AI Cloud Developer AI-200 Series
Good luck to you passing the AI-200 Exam!
However, the more preparation you have, the less luck you will need. 🙂
Visit this post to see the list of all the certification preparation hubs available on The Data Community.
The Data Community 