Azure AI Cloud Developer Associate AI-200 Course

Azure AI Cloud Developer Associate AI-200 Course Outline

Module 1: Store and Manage Containers in Azure Container Registry

• Introduction
• Registries, Repositories, and Artifacts
• Build and Run Images with ACR Tasks
• Tag and Version Images
• Exercise - Build and Manage a Container Image with ACR Tasks

Module 2: Deploy Containers to Azure App Service

• Introduction
• Deploy Containers to Azure App Service
• Configure Container Runtime Behavior
• Configure Application Settings
• Observe and Troubleshoot Containerized Apps
• Exercise - Deploy a Container to Azure App Service

Module 3: Deploy Containers to Azure Container Apps

• Introduction
• Explore Container Apps Environments
• Deploy a Container App Using the Azure CLI and YAML
• Configure Runtime Settings with Environment Variables and Secrets
• Configure Image Pull Authentication for Private Registries
• Verify Deployments with Logs and Status
• Exercise - Deploy a Containerized Backend API to Container Apps

Module 4: Manage Containers in Azure Container Apps

• Introduction
• Update Images and Manage Revisions Safely
• Manage the Container App Lifecycle
• Monitor Logs and Troubleshoot Issues
• Configure Health Probes and Troubleshoot Failures
• Optimise Container Resources and Scaling
• Exercise - Diagnose and Fix a Failing Deployment

Module 5: Scale Containers in Azure Container Apps

• Introduction
• Configure Scale Rules
• Implement Event-Driven Scaling with KEDA
• Apply KEDA Scalers for Custom Workloads
• Select Compute Resources for Performance and Cost
• Choose and Apply Revision Modes
• Exercise - Configure Autoscaling Using KEDA Triggers

Module 6: Deploy Applications to Azure Kubernetes Service

• Introduction
• Create Kubernetes Deployment Manifests
• Expose Applications in Azure Kubernetes Services
• Deploy Applications to Azure Kubernetes Services
• Exercise - Deploy an AI Inference API to Azure Kubernetes Service

Module 7: Configure Applications on Azure Kubernetes Service

• Introduction
• Define ConfigMaps for Application Settings
• Implement Secrets for Sensitive Data
• Attach Persistent Storage to an App
• Exercise - Configure Apps on Azure Kubernetes Service

Module 8: Monitor and Troubleshoot Applications on Azure Kubernetes Service

• Introduction
• Monitor Application Logs and Metrics
• Troubleshoot Pods and Services
• Verify Service Connectivity and Endpoints
• Exercise - Troubleshoot Apps on Azure Kubernetes Service

Module 9: Build Queries for Azure Cosmos DB for NoSQL

• Introduction
• Explore Azure Cosmos DB for NoSQL
• Implement the Azure Cosmos DB for NoSQL SDK
• Query Azure Cosmos DB for NoSQL
• Exercise - Build a RAG Document Store on Azure Cosmos DB for NoSQL

Module 10: Implement Vector Search on Azure Cosmos DB for NoSQL

• Introduction
• Store and Retrieve Embeddings in Azure Cosmos DB
• Execute Vector Similarity Queries for Semantic Search
• Combine Vector Similarity Results with Metadata Filtering
• Use the Change Feed to Trigger Embedding Refresh
• Exercise - Build a Semantic Search Application with Azure Cosmos DB for NoSQL

Module 11: Optimise Query Performance for Azure Cosmos DB for NoSQL

• Introduction
• Understand Indexes in Azure Cosmos DB
• Configure Range and Composite Indexes
• Tune Vector Indexes for Embedding Workloads
• Reduce RU Costs with Strategic Indexing
• Choose Consistency Levels for Optimal Performance
• Exercise - Optimise Query Performance with Vector Indexes on Azure Cosmos DB for NoSQL

Module 12: Build and Query with Azure Database for PostgreSQL

• Introduction
• Explore Azure Database for PostgreSQL
• Connect to PostgreSQL
• Create and Manage Schemas
• Query Data
• Integrate SDKs and Applications
• Exercise - Build an Agent Tool Backend on Azure Database for PostgreSQL

Module 13: Implement Vector Search with Azure Database for PostgreSQL

• Introduction
• Store and Query Embeddings with pgvector
• Perform Fast Vector Similarity Search
• Manage Index Lifecycle and Embedding Updates
• Run Vector Similarity Search for Semantic Retrieval
• Implement Retrieval Patterns for RAG Pipelines
• Exercise - Implement Vector Search on Azure Database for PostgreSQL

Module 14: Optimise Vector Search in Azure Database for PostgreSQL

• Introduction
• Tune PostgreSQL for pgvector
• Choose and Configure Vector Indexes
• Optimise Data Layout
• Scale for High-Volume Workloads
• Connection Optimisation
• Exercise - Optimise Vector Search Performance in Azure Database for PostgreSQL

Module 15: Implement Data Operations in Azure Managed Redis

• Introduction
• Explore Azure Managed Redis
• Client Libraries and Development Best Practices
• Implement Data Operations
• Exercise - Perform Data Operations in Azure Managed Redis

Module 16: Implement Event Messaging with Azure Managed Redis

• Introduction
• Publish and Subscribe to Events with Redis Pub/Sub
• Implement Task Queues with Redis Streams
• Choose Between Broadcast and Coordinated Distribution
• Exercise - Publish and Subscribe to Events in Azure Managed Redis

Module 17: Implement Vector Storage in Azure Managed Redis

• Introduction
• Index and Query Vector Data
• Choose Vector Types and Indexing Strategies
• Optimise Redis Data Structures for Vector Storage
• Exercise - Implement Semantic Search in Azure Managed Redis

Module 18: Queue and Process AI Operations with Azure Service Bus

• Introduction
• Explore Azure Service Bus Concepts and Messaging in AI Architectures
• Choose Between Queues and Topics with Subscriptions
• Structure Messages for AI Workloads
• Process Messages Reliably
• Exercise - Process Messages with Azure Service Bus

Module 19: Develop Event-Driven AI Workflows with Azure Event Grid

• Introduction
• Understand Azure Event Grid Concepts and Event-Driven Patterns for AI Solutions
• Work with Event Schemas and Properties
• Configure Delivery and Retry Policies for Reliable Event Processing
• Publish Custom Events from AI Applications
• Exercise - Publish and Receive Events with Azure Event Grid

Module 20: Build Serverless AI Backends with Azure Functions

• Introduction
• Understand Azure Functions Hosting and Scaling for AI Workloads
• Set Up the Local Development Environment for Functions
• Create Triggers and Bindings for AI Integration Patterns
• Manage Secrets and Configuration in Functions
• Configure Identity and Access for Functions
• Exercise - Create an MCP Server with Azure Functions

Module 21: Manage Application Secrets with Azure Key Vault

• Introduction
• Store and Organise Secrets, Keys, and Certificates
• Retrieve Secrets Using Azure SDK Client Libraries
• Handle Secret Versioning and Rotation
• Implement Caching Strategies to Reduce Key Vault Calls
• Exercise - Manage Secrets with Azure Key Vault

Module 22: Manage Application Settings with Azure App Configuration

• Introduction
• Connect to App Configuration from Application Code
• Organise Settings with Labels and Feature Flags
• Reference Key Vault Secrets from App Configuration
• Decide What to Store in App Configuration vs Key Vault
• Exercise - Retrieve Settings and Secrets from Azure App Configuration

Module 23: Instrument an App with OpenTelemetry

• Introduction
• Explore OpenTelemetry and Its Role in Observability
• Add the OpenTelemetry SDK to an Application
• Configure Spans and Traces
• Export Telemetry to Azure Monitor
• Debug Distributed Flows with Trace Data
• Exercise - Instrument an App with the OpenTelemetry SDK

Module 24: Analyse App Telemetry with Logs and Metrics

• Introduction
• Write Basic KQL Queries
• Explore Logs for Errors and Performance
• Build Dashboards for App Telemetry
• Create Workbooks for Interactive Analysis
• Set Alerts for App Failures and Anomalies
• Exercise - Query Logs with KQL

Who Should Attend This Azure AI Cloud Developer Associate AI-200 Course

This course is designed for professionals who want to build and manage AI solutions on cloud platforms using Azure. It is ideal for individuals looking to develop, deploy, and maintain AI-powered applications in real-world environments. This course is particularly beneficial for the following professionals:

• AI Developers
• Cloud Developers
• Software Engineers
• Data Professionals
• Application Developers
• IT Professionals

Prerequisites of the Azure AI Cloud Developer Associate AI-200 Course

There are no formal prerequisites for this course. However, a basic understanding of programming, cloud computing concepts, and familiarity with Microsoft Azure may be beneficial.

Azure AI Cloud Developer Associate AI-200 Overview

The Azure AI Cloud Developer Associate AI-200 Course provides a practical understanding of how to build and deploy AI solutions using Microsoft Azure cloud services. It introduces key concepts required to develop intelligent applications in cloud environments.

The training focuses on integrating AI services, managing deployments, and monitoring performance of AI-powered applications. Organisations can use these skills to build scalable, reliable, and efficient AI systems in the cloud.

This 5-Day course offered by The Knowledge Academy equips delegates with the knowledge and skills required to develop and manage AI solutions effectively using Azure cloud platforms. It also helps professionals understand how to design, integrate, and optimise AI applications for real-world business use cases.

Azure AI Cloud Developer Associate AI-200 Course Objectives

• To understand how to build AI solutions using Azure cloud services
• To develop and deploy AI applications in cloud environments
• To integrate AI services into applications
• To monitor and optimise AI system performance
• To manage cloud-based AI solutions securely and efficiently
• To apply AI development practices in real-world scenarios

Upon completing this course, delegates will be able to build, deploy, and manage AI solutions effectively using Azure cloud platforms.

What’s Included in this AI-200 Course?

• World-Class Training Sessions from Experienced Instructors 
• Azure AI Cloud Developer Associate AI-200 Certificate
• Digital Delegate Pack  
• Interactive Learning with 24×7 Support