Review the Microsoft Azure AI Cloud Developer Associate (AI-200) scope, Azure application patterns, data-service decisions, and troubleshooting traps before practicing in IT Mastery.
AI-200 is less about naming Azure services and more about choosing the right implementation boundary for AI-enabled cloud applications. Use this page to review container hosting, Azure data services, service integration, and operational troubleshooting before you start timed practice.
Use this with practice. Review the implementation checklist, then try a diagnostic or open the full AI-200 route in IT Mastery.
| Field | Detail |
|---|---|
| Issuer | Microsoft |
| Certification lane | Microsoft Azure AI Cloud Developer Associate |
| Exam code | AI-200 |
| Main scope | Azure application development for containerized, AI-enabled, data-connected solutions |
| IT Mastery status | Live AI-200 practice available |
| Domain | Practice weight | What to know | Common trap |
|---|---|---|---|
| Develop containerized solutions on Azure | 23% | App Service containers, Container Apps, AKS boundaries, images, probes, scaling, configuration, and deployment artifacts | Solving app lifecycle problems by changing platform infrastructure the app team does not own |
| Develop AI solutions by using Azure data management services | 29% | PostgreSQL vector search, Cosmos DB, metadata filtering, indexing, document storage, and source citations | Retrieving first and filtering later, which breaks relevance or tenant isolation |
| Connect to and consume Azure services | 24% | SDK clients, managed identity, queues, events, APIs, retries, and connection lifecycle | Recreating clients per request or storing secrets in code |
| Secure, monitor, and troubleshoot Azure solutions | 24% | Observability, logs, app settings, Key Vault, RBAC, performance evidence, and root-cause selection | Treating symptoms with scale-out before reading the evidence |
| Distinction | How to decide |
|---|---|
| Container Apps vs AKS | Container Apps fits managed container apps and event-driven scale; AKS fits cluster-level Kubernetes control and platform-managed workloads. |
| App setting vs image config | Use app settings for environment-specific runtime values; do not bake secrets or environment values into images. |
| Managed identity vs connection string | Prefer managed identity for Azure service access when supported; connection strings are harder to rotate and protect. |
| Metadata filter before vector ranking | Filter by tenant, source, category, or date before ranking when the filter defines the valid candidate set. |
| Management-plane SDK vs data-plane SDK | Management clients configure resources; data-plane clients read and write application data. |
| Scale problem vs query-shape problem | Scale when resources are saturated; change query shape when the app reads too much or filters too late. |
| Queue retry vs dead-letter | Retry handles temporary failure; dead-letter queues isolate messages that cannot be processed successfully. |
| Logs vs metrics | Metrics show signals quickly; logs explain detailed events, requests, failures, and query behavior. |
Use the free AI-200 diagnostic as an evidence-reading test. After every miss, ask what the scenario actually proved: a deployment-boundary issue, a data-query issue, a service-integration issue, or an operations issue. Then drill that topic page before returning to mixed sets.
Move to timed mocks when you can explain not only the right Azure service, but also why the wrong options change the ownership boundary, ignore the evidence, or create unnecessary architecture.