Microsoft has retired the long-running AZ-204 exam and replaced it with a new certification built specifically for the AI era. If you are looking for the latest Microsoft AI-200 Exam Question bank to prepare with, you are in the right place. The Exam AI-200: Developing AI Cloud Solutions on Azure leads to the Microsoft Certified: Azure AI Cloud Developer Associate credential, and it validates a very different skill set than its predecessor. Instead of general web-app development, AI-200 tests your ability to build, secure, and monitor the back-end infrastructure that powers production AI systems on Azure.
If you are an Azure developer wondering whether to take AI-200 or where even to start, this guide breaks down everything you need to know: what the exam covers, how it differs from AZ-204, the four skill domains and their weightings, and a practical study plan.
What Is the AI-200 Exam?
AI-200 is an Associate-level certification exam that assesses your ability to contribute to all phases of implementing AI solutions on Azure, with heavy emphasis on back-end services and components. That includes requirements gathering, design, development, deployment, security, and monitoring.
Unlike exams such as AI-102, which focus on calling and orchestrating Azure AI services directly (vision, language, generative AI, agents), AI-200 tests the platform underneath those services. In simple terms, AI-102 teaches you to use the AI, while AI-200 teaches you to build and run the cloud infrastructure that the AI depends on.
## Why AI-200 Replaced AZ-204
AZ-204 was Microsoft's general-purpose Azure Developer Associate exam for years, covering compute, storage, security, monitoring, and service integration. AI-200 keeps that same structural DNA but shifts the entire focus toward AI workloads. The main additions are vector-capable data services such as Cosmos DB vector search, PostgreSQL with pgvector, and Azure Managed Redis, along with modern observability tools like OpenTelemetry and KQL. Developers who already hold AZ-204 or have equivalent experience will find the compute, messaging, and security sections familiar, and should concentrate most of their study time on the new AI data services.
AI-200 Skill Domains and Weightings
The exam is organized into four official domains:
Develop containerized solutions on Azure (20 to 25 percent). This covers building, storing, versioning, and managing container images with Azure Container Registry, deploying to Azure Container Apps and Azure Kubernetes Service, implementing event-driven scaling with KEDA, and monitoring containerized workloads by inspecting logs, events, and connectivity.
Develop AI solutions by using Azure data management services (25 to 30 percent). This is the largest domain and the one most different from AZ-204. It focuses on connecting to Azure Cosmos DB for NoSQL through the SDK, implementing vector search across Cosmos DB, PostgreSQL with pgvector, and Azure Managed Redis, and designing retrieval-augmented generation, or RAG, architectures using Azure AI Search.
Connect to and consume Azure services (20 to 25 percent). This domain tests event-driven and message-based architecture using Azure Event Grid and Service Bus, building serverless APIs with Azure Functions including triggers and bindings, and integrating the Azure SDK across services.
Secure, monitor, and troubleshoot Azure solutions (20 to 25 percent). This covers eliminating hard-coded secrets through Microsoft Entra ID managed identities and Azure Key Vault references, implementing distributed observability, and using KQL for monitoring and troubleshooting.
Core Technologies Covered on the Exam
Across the four domains, expect scenario-based questions built around Azure Container Apps and AKS, Azure Cosmos DB for NoSQL and its vector capabilities, Azure Database for PostgreSQL with pgvector, Azure Managed Redis, Azure AI Search and RAG patterns, Azure AI Foundry and the model catalog, Azure Functions, Event Grid, and Service Bus, Microsoft Entra ID and Key Vault, and KQL plus OpenTelemetry for observability. Python is the primary programming language referenced in the official skills outline, so hands-on Python experience with the Azure SDK is valuable preparation.
## Core Technologies Covered on the Exam
Across the four domains, expect scenario-based questions built around Azure Container Apps and AKS, Azure Cosmos DB for NoSQL and its vector capabilities, Azure Database for PostgreSQL with pgvector, Azure Managed Redis, Azure AI Search and RAG patterns, Azure AI Foundry and the model catalog, Azure Functions, Event Grid, and Service Bus, Microsoft Entra ID and Key Vault, and KQL plus OpenTelemetry for observability. Python is the primary programming language referenced in the official skills outline, so hands-on Python experience with the Azure SDK is valuable preparation.
Who Should Take AI-200
This certification fits Azure developers, AI application developers, cloud engineers, and back-end developers who want to prove they can build intelligent, production-ready applications on Azure rather than call pre-built AI APIs. It is also the natural next step for anyone holding an expiring AZ-204 certification.
## How to Prepare for the AI-200 Exam
Candidates who are already comfortable with Container Apps, AKS, and an Azure SDK language should plan for roughly four to six weeks of focused study, weighted toward the data management domain. Developers newer to Azure application development should budget closer to ten to twelve weeks.
A practical study path looks like this. Start with the official Microsoft Learn training course for AI-200 and the published study guide to understand the full skills outline. Get hands-on with Azure Container Apps and AKS by deploying and scaling a real containerized workload. Spend extra time on vector search across Cosmos DB, pgvector, and Redis since this is the highest-weighted domain. Practice writing and reading KQL queries for observability, since this is a newer requirement compared to AZ-204. Finish with scenario-based practice tests that mirror the exam's real-world architecture questions rather than simple recall questions.
This short walkthrough covers real exam-style scenarios and is worth watching before test day: https://www.youtube.com/shorts/hLJ4DHaXYCs
Final Thoughts
AI-200 represents Microsoft's clearest signal yet that AI development on Azure has moved beyond simple API calls into full cloud-native architecture. Passing it proves you can deploy containers, manage vector-enabled data services, connect event-driven pipelines, and secure and monitor the entire system in production. Start with the official Microsoft Learn study guide, build hands-on experience with the data services domain first, and work through scenario-based practice questions before you sit the exam.
