AI-200: Azure AI Cloud Developer Associate

This course contains the use of artificial intelligence.

AI-200, is a meticulously structured Udemy course aimed at IT professionals seeking to pass the AI-200 exam. This course systematically walks you through the initial setup to advanced implementation with real-world applications.

By passing AI-200, you're gaining proficiency in the highly recognized Microsoft Azure ecosystem.

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

• Trace distributed systems by using OpenTelemetry SDKs

• Write KQL queries to analyze logs and metrics

This course contains promotional materials.