Microsoft AI-200: Developing AI Cloud Solutions on Azure Training - OEM Certkit

The CertKit: Microsoft AI-200: Developing AI Cloud Solutions on Azure teaches developers how to create, monitor and troubleshoot AI solutions on Microsoft Azure. This training is designed for developers who build backend and AI-driven applications and need practical skills in Azure compute, containerization, serverless development, event-driven architectures, data services, security and observability.

Demo Microsoft AI-200: Developing AI Cloud Solutions on Azure Training

In this CertKit, you will learn how to host applications with Azure Container Apps, Azure Kubernetes Service and Azure App Service. You will build serverless APIs with Azure Functions and integrate services using event-driven and message-based architectures such as Azure Service Bus and Azure Event Grid.

You will also work with Azure data services that support AI workloads, including Azure Cosmos DB for NoSQL, Azure Database for PostgreSQL with pgvector and Azure Managed Redis for caching, streaming and vector search. You will learn how to connect services, orchestrate AI workflows and build secure, scalable and observable AI-driven applications on Azure.

Prerequisites

Learners should have hands-on experience with Azure fundamentals and practical knowledge of building cloud applications. Familiarity with containers, serverless compute, event-driven architectures, core AI concepts and integrating Azure AI services into applications is recommended.

What will you learn?

• Implement container application hosting on Azure
• Deploy and manage apps on Azure Container Apps
• Deploy and monitor applications on Azure Kubernetes Service
• Develop AI solutions with Azure Cosmos DB for NoSQL
• Develop AI solutions with Azure Database for PostgreSQL
• Enhance AI solutions with Azure Managed Redis
• Integrate backend services for AI solutions
• Manage application secrets and configuration for AI solutions
• Observe, monitor and troubleshoot applications on Azure

Who should attend?

This CertKit is designed for developers who build backend and AI-driven applications on Microsoft Azure.

The training is especially suitable for:

• Azure Developers
• AI Developers
• Backend Developers
• Cloud Developers
• Software Developers
• DevOps Engineers
• Cloud Engineers
• Developers working with containers and serverless compute
• Professionals who want to build and manage AI workloads on Azure

CertKit content

E-learning courses:

AI-200 - Azure AI Cloud Solutions: Azure Container Registry Image Management

Course: 57 Minutes

• Course Overview
• Understanding the AI-200 Container Image Hosting Scope
• Designing Container Image Repositories for AI Services
• Building and Tagging Images for Deployment Readiness
• Version Images and Management of Immutable References
• Secure Registry Access for Deployment Targets
• Image Preparation for App Service, Container Apps, and AKS Deployments
• Pushing and Inspecting an AI Service Image in Azure Container Registry
• Retagging and Validating Image Versions for Rollback
• Course Summary

AI-200 - Azure AI Cloud Solutions: Automating Container Builds with ACR Tasks

Course: 59 Minutes

• Course Overview
• ACR Tasks Build Automation
• Quick Tasks for On-Demand Image Builds
• Source-Control Triggered Builds
• Base Image Updates and Scheduled Builds
• Run Variables and Task Contexts
• Multi-Step Build and Test Tasks
• Running a Quick Task to Build an Image in ACR
• Creating a Triggered ACR Task Definition
• Reviewing a Multi-Step ACR Task Workflow
  Course Summary

AI-200 - Azure AI Cloud Solutions: Containerized AI Services & Azure App Service

Course: 1 Hour, 9 Minutes

• Course Overview
• Azure App Service for Containerized AI APIs
• Custom Container Image Sources
• Supplying Environment Variables with Azure App Service App Settings
• Protecting Container Secrets with Key Vault References
• Managing Startup Behavior and Health Expectations
• Reviewing Logs for Azure App Service Container Failures
• When to Move Beyond Azure App Service
• Deploying Containerized AI API to Azure App Service
• Configuring App Settings and Key Vault References
• Diagnosing Azure App Service Container Startup Failures
• Course Summary

AI-200 - Azure AI Cloud Solutions: Azure Container Apps Environments, Revisions, & KEDA Scaling

Course: 1 Hour, 17 Minutes

• Course Overview
• Azure Container Apps Hosting Patterns
• Container Apps Environments and Ingress
• Environment Variables and Secrets
• Controlled Change with Revisions
• Traffic Splitting Across Container App Revisions
• KEDA-Based Event-Driven Scaling
• Replica Limits and Scaling Behavior
• Logs and Connectivity in Container Apps
• Deploying a Container App from Azure Container Registry
• Creating a New Revision and Shifting Traffic
• Configuring a KEDA Scale Rule for Queue Pressure
• Inspecting Container App Logs and Replica Events
• Course Summary

AI-200 - Azure AI Cloud Solutions: AKS Deployment & Troubleshooting

Course: 1 Hour, 14 Minutes

• Course Overview
• Kubernetes Manifests for AI Workloads
• Registry Images and Environment Settings
• Deploying and Updating Applications with kubectl
• Inspecting Pods, Events, and Container Logs
• Troubleshooting Service Connectivity in AKS
• Azure Monitor and Container Insights for AKS
• Comparing AKS and Container Apps Diagnostic Signals
• Deploying an AI Worker Manifest to AKS
• Updating an Image Tag and Observing the Rollout
• Diagnosing a Failed Pod with Events and Logs
• Analyzing AKS Workload Health in Azure Monitor
• Course Summary

AI-200 - Azure AI Cloud Solutions: Cosmos DB for NoSQL & RU Optimization

Course: 1 Hour, 1 Minutes

• Course Overview
• Connecting to Cosmos DB for NoSQL with SDKs
• Point Reads and SQL Queries
• Partition Keys Usage for Efficient Access
• Indexing Policy Tuning for Query Patterns
• Consistency Levels for AI Workloads
• Estimating and Reducing RU Consumption
• Connecting and Querying Cosmos DB with Python
• Inspecting RU Charges for Query Variants
• Reviewing Indexing and Consistency Tradeoffs
• Course Summary

AI-200 - Azure AI Cloud Solutions: Cosmos DB Vector Search & Change Feed Processing

Course: 1 Hour, 4 Minutes

• Course Overview
• Cosmos DB Documents for Embedding Storage
• Vector Policies and Indexes
• Vector Similarity Search
• Metadata Filters in Semantic Retrieval
• Cosmos DB Vector Search in RAG Patterns
• Change Feed Processing
• Embeddings Refresh with Change Feed Processor
• Configuring a Cosmos DB Vector Policy and Query
• Performing Vector Searches with Metadata Filters
• Implementing a Change Feed Processor for AI Updates
• Course Summary

AI-200 - Azure AI Cloud Solutions: PostgreSQL Schema, Indexing, & pgvector Foundations

Course: 1 Hour, 16 Minutes

• Course Overview
• Querying Azure Database for PostgreSQL with SDKs
• Table Structures and Data Types for AI Metadata
• Enabling pgvector in Azure Database for PostgreSQL
• Storing Embeddings and Metadata Together
• Running Basic Vector Similarity Queries
• Foundational Indexing Strategies
• Query Plans for Schema Feedback
• Connecting to Azure Database for PostgreSQL and Running Queries
• Enabling pgvector and Creating an Embedding Table
• Inserting Embeddings and Running Similarity Search
• Inspecting Index Choices for a Vector Table
• Course Summary

AI-200 - Azure AI Cloud Solutions: PostgreSQL Vector RAG Performance & Connection Optimization

Course: 1 Hour, 22 Minutes

• Course Overview
• pgvector Latency Drivers
• Vector Indexing Strategies
• Vector Search with Metadata Filters
• Resource Configuration for Vector Workloads
• pgvector Compute Overhead
• Connection Pooling with PgBouncer
• Throughput Tuning and Connection Latency
• PostgreSQL Performance Signals
• Comparing Indexed and Unindexed Vector Queries
• Applying Metadata Filters to a PostgreSQL RAG Query
• Configuring PgBouncer for Connection Pooling
• Using Metrics to Identify PostgreSQL Resource Pressure
• Course Summary

AI-200 - Azure AI Cloud Solutions: Azure Managed Redis Data Operations for AI Caching

Course: 55 Minutes

• Course Overview
• Azure Managed Redis in AI Workloads
• Cache-Aside Data Operations
• Redis Data Structures for Cached Results
• Expiration and Invalidation Policies
• Key Expiration, Eviction, and Deletion Diagnosis
• Storing and Retrieving AI Cache Values in Redis
• Applying Expiration and Invalidation to Cached Context
• Troubleshooting Missing Cache Keys in Azure Managed Redis
• Course Summary

AI-200 - Azure AI Cloud Solutions: Azure Managed Redis Vector Indexing & Similarity Search

Course: 54 Minutes

• Course Overview
• Redis as a Vector Store
• Hash vs. JSON Vector Storage
• Vector Indexes with RediSearch
• Similarity Queries for Semantic Retrieval
• Redis vs. Other Azure Vector Options
• Creating a Redis Vector Index
• Querying Redis for Similar Vectors
• Reviewing a Semantic Cache and Vector Retrieval Pattern
• Course Summary

AI-200 - Azure AI Cloud Solutions: Azure Service Bus for Back-End AI Operations

Course: 1 Hour

• Course Overview
• Service Bus for AI Back-End Operations
• Choosing Queues for Point-to-Point Processing
• Using Topics and Subscriptions for Fan-Out
• Message Settlement and Processing Outcomes
• Managing Dead-Letter Queue Failures
• Designing Service Bus Processing for Throughput
• Sending and Receiving Service Bus Queue Messages
• Routing Messages Through Topics and Subscriptions
• Inspecting and Recovering Dead-Letter Queue Messages
• Course Summary

AI-200 - Azure AI Cloud Solutions: Azure Event Grid for Event-Driven AI Workflows

Course: 1 Hour, 8 Minutes

• Course Overview
• Event Grid for AI Event Routing
• System Topics and Custom Topics
• Structure Custom Events for AI Workflows
• Applying Event Grid Filters
• Handling Delivery Retries and Failures
• Connecting an Event Grid to Azure Functions
• Selecting an Event Grid Or Service Bus by Requirement
• Creating a Custom Event Grid Topic and Event
• Configuring Event Filters for an AI Workflow
• Reviewing an Event Grid Retry and Delivery Behavior
• Course Summary

AI-200 - Azure AI Cloud Solutions: Building Serverless AI APIs with Azure Functions

Course: 1 Hour, 21 Minutes

• Course Overview
• Azure Functions in AI Back-End Components
• HTTP-Triggered Serverless APIs
• Input and Output Bindings
• Process Service Bus Messages with Functions
• Handling Event Grid Events with Functions
• Function Inputs and Responses for AI APIs
• Using Azure SDKs Inside Function Code
• Planning Runtime Limits and Long-Running Work
• Creating an HTTP-Triggered AI Function
• Adding a Service Bus Trigger to Process AI Work
• Adding an Output Binding for Back-End Results
• Calling an Azure Service from Function Code
• Course Summary

AI-200 - Azure AI Cloud Solutions: Deploying Azure Functions with Triggers & Bindings

Course: 1 Hour, 5 Minutes

• Course Overview
• Function App Resource Settings
• Azure Functions Deployment Technologies
• App Settings and Connection Configuration
• Managed Identity for Function Service Access
• Monitoring Function Executions and Failures
• Resolving Trigger and Binding Configuration Problems
• Deploying a Python Function App to Azure
• Configuring Function App Settings and Identity
• Inspecting Function Execution Logs and Trigger Failures
• Course Summary

AI-200 - Azure AI Cloud Solutions: Consuming Services with SDKs in Back-End AI Components

Course: 1 Hour, 15 Minutes

• Course Overview
• Azure SDKs in AI Back-End Components
• Authentication with DefaultAzureCredential
• Creating Clients and Managing Client Lifetime
• Handling Retries, Timeouts, and Service Errors
• Async and Pagination Patterns
• Externalizing Configuration and Secret Retrieval
• Service Boundaries for SDK Calls
• Implementing Authentication with DefaultAzureCredential
• Implementing a Reusable Azure SDK Client Wrapper
• Configuring Retry and Timeout Settings for Azure SDK Calls
• Managing Configuration, Secrets, and Azure Service Integration
• Course Summary

AI-200 - Azure AI Cloud Solutions: Key Vault Secrets, Rotation, & Secure Retrieval

Course: 1 Hour, 10 Minutes

• Course Overview
• Managing the Key Vault for AI Application Secrets
• Storing and Retrieving Secrets Securely
• Authenticating Secret Retrieval with Managed Identity
• Controlling Access to Secret Values
• Planning a Secret Rotation for AI Integrations
• Caching Secrets Without Losing Rotation Safety
• Monitoring and Auditing Secret Access
• Retrieving a Key Vault Secret from Python
• Configuring Managed Identity Access to Key Vault
• Reviewing a Secret Rotation Workflow
• Course Summary

AI-200 - Azure AI Cloud Solutions: Azure App Configuration for Runtime Settings

Course: 51 Minutes

• Course Overview
• Using App Configuration for Runtime Settings
• Designing Key-Value and Label Structures
• Retrieval of Configuration from Python Applications
• Integrating App Configuration with Key Vault References
• Applying Configuration Best Practices
• Creating and Retrieving App Configuration Values
• Using Labels for Environment-Specific Settings
• Reviewing Key Vault References in App Configuration
• Course Summary

AI-200 - Azure AI Cloud Solutions: Distributed Tracing with OpenTelemetry SDKs

Course: 1 Hour, 14 Minutes

• Course Overview
• Distributed Tracing for AI Workloads
• OpenTelemetry SDKs and Azure Monitor
• Propagation of Context Across Service Boundaries
• Custom Span Creation for AI Processing Steps
• Tracing Dependencies and Failure Points
• Sampling and Telemetry Volume Configuration
• Trace Timeline Interpretation for Troubleshooting
• Managing OpenTelemetry for a Python AI Service
• Adding Custom Spans Around Retrieval and Model Calls
• Tracing a Message-Based Processing Flow
• Diagnosing Latency from a Distributed Trace
• Course Summary

AI-200 - Azure AI Cloud Solutions: KQL Diagnostics for Logs, Metrics, & AI Workloads

Course: 1 Hour, 23 Minutes

• Course Overview
• Azure Monitor Logs for AI Diagnostics
• KQL Query Structuring with Tables and Filters
• Project, Sort, and Summarize Diagnostic Data
• Time Window and Trend Analysis
• Container and AKS Diagnostic Signal Querying
• Azure Functions Execution Data Querying
• Correlating Logs, Metrics, and Traces
• Reusable Diagnostic Query Pattern Building
• Running Basic KQL Queries in Log Analytics
• Analyzing Container Logs with KQL
• Querying Function Execution and Failure Trends
• Creating an Incident Query Pack for AI Workloads
• Course Summary

AI-200 - Azure AI Cloud Solutions: Exam Preparation and Review

Course: 22 Minutes

• Course Overview
• AI-200 Certification Scope and Prerequisites
• AI-200 Exam Format and Logistics
• Domain 1 Review: Develop Containerized Solutions on Azure (20–25%)
• Domain 2 Review: AI Solutions Using Azure Data Management Services (25–30%)
• Domain 3 Review: Connect to and Consume Azure Services (20–25%)
• Domain 4 Review: Secure, Monitor, and Troubleshoot Azure Solutions (20–25%)
• AI-200 Exam Question Types and Approach Strategies
• Common Exam Traps and Time Management
• Course Summary

AI-200 - Azure AI Cloud Solutions: End-to-End Troubleshooting Playbooks

Course: 1 Hour, 10 Minutes

• Course Overview
• Cross-Service Troubleshooting Sequence
• Container Deployment and Runtime Failures
• Messaging and Event Processing Failures
• Data Retrieval and Vector Search Problems
• Secrets and Configuration Failures
• Using Traces and KQL to Isolate Root Cause
• Troubleshooting a Failed Containerized AI API
• Troubleshooting a Dead-Lettered AI Work Message
• Troubleshooting Stale Retrieval Context
• Tracing and Querying an End-to-End AI Workflow Failure
• Course Summary

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