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Exam overview — AI-200 ​

Exam AI-200: Developing AI Cloud Solutions on Azure leads to the Microsoft Certified: Azure AI Cloud Developer Associate credential. It targets developers who build, integrate, secure, and monitor AI solutions on Azure, with an emphasis on back-end services and components.

At a glance ​

CredentialAzure AI Cloud Developer Associate
Length~120 minutes
Pass score700 / 1000 (scaled)
Question stylesMultiple choice, multiple response, case studies, drag-and-drop, possibly labs
RenewalAnnually, via a free Microsoft Learn assessment

Freshly out of beta

Microsoft hasn't yet published a dated "Skills measured as of…" heading or a change log for AI-200, and the official practice assessment isn't available yet either — both usually appear within about eight weeks of an exam going generally available. Re-check the study guide before you sit it.

Skills measured — the four domains ​

#DomainWeight
1Develop containerized solutions on Azure20–25%
2Develop AI solutions using Azure data management services25–30%
3Connect to and consume Azure services20–25%
4Secure, monitor, and troubleshoot Azure solutions20–25%

What you should already be proficient in ​

  • Azure SDKs and third-party SDKs used on Azure
  • Azure data management services (Cosmos DB, PostgreSQL, Redis)
  • Azure monitoring and troubleshooting (Azure Monitor, Log Analytics, KQL)
  • Azure messaging and eventing (Service Bus, Event Grid)
  • Vector databases and embedding-based retrieval
  • Python programming — this is what the official study guide lists. The exam itself is language-agnostic in how it scores you; only code snippets differ. The questions and snippets on this site are written in C# / .NET, so read the Python prerequisite as "you can read code in the SDK of your choice".
  • Implementing containerized applications on Azure (ACR, Container Apps, AKS)

Key cross-cutting themes ​

These ideas show up across multiple domains — make sure they are second nature:

  • Managed identity + RBAC over stored secrets — for ACR pulls, Key Vault, Cosmos, etc.
  • Vector search / RAG — the same embedding-and-retrieve pattern recurs in Cosmos DB, PostgreSQL/pgvector, and Redis.
  • Event-driven scaling — KEDA in Container Apps, queue/topic triggers in Functions.
  • Observability — OpenTelemetry tracing into Application Insights, analyzed with KQL.

Ready? Start with Domain 1 or jump to the study plan.

Unofficial study hub. Content grounded in the official Microsoft Learn study guides.