Azure AI-200 Learning Lab
Welcome to the hands-on learning workspace for Microsoft Certified Azure AI Cloud Developer Associate (AI-200).
This repository is organized into progressive, code-first modules designed to build real-world, cloud-native AI systems on Microsoft Azure.
Learning Roadmap & Exam Domains
Module Breakdown
| Module | Core Topics | Key Azure Services / SDKs |
|---|---|---|
01-ai-foundry-identity-inference | Zero-trust auth, Entra ID, chat completions, streaming, retry policies | azure-identity, azure-ai-inference, openai |
02-vector-search-and-data | Embeddings, vector indexing, hybrid search (BM25 + vector), RAG | azure-search-documents, azure-cosmos |
03-container-apps-and-compute | Microservice AI APIs, ACA scaling, health probes, Docker | Azure Container Apps, Azure Container Registry, FastAPI |
04-event-driven-ai-pipelines | Async batch processing, message queuing, dead-lettering | Azure Service Bus, Event Grid, Storage Queues |
05-observability-and-evaluation | Distributed tracing, token tracking, latency, groundness evaluation | Azure Monitor, Application Insights, OpenTelemetry |
Key Cloud AI Principle: Zero-Trust & Passwordless
In AI-200, hardcoded API keys are considered an anti-pattern.
Production backends use Microsoft Entra ID Role-Based Access Control (RBAC) and Managed Identities (DefaultAzureCredential) so secrets never leak into code or environment files.