Build and Deploy AI Cloud Solutions on Microsoft Azure.

  • Why get trained: Learn how to build AI-driven applications using Azure Functions, Azure Service Bus, Cosmos DB, PostgreSQL with pgvector, Azure Managed Redis and containerized Azure compute services.
  • Why it matters: Azure AI skills help organizations create secure, scalable and observable AI applications that improve automation, integration and business performance on Azure.
  • Who should attend: Developers, software engineers and technical professionals who build backend, cloud-native or AI-powered applications on Microsoft Azure.

Build practical Azure AI development skills with Trainocate Malaysia and prepare for the Microsoft Certified: Azure AI Cloud Developer Associate certification. HRD Corp Claimable.

Overview

This course teaches developers how to create, monitor, and troubleshoot AI solutions on Microsoft Azure.

Students will learn how to implement Azure compute and containerization patterns to host applications, build serverless APIs with Azure Functions, and integrate services using event‑driven and message‑based architectures such as Azure Service Bus and Event Grid.

The course also covers working with Azure data services that support AI workloads, including designing and querying solutions with Cosmos DB for NoSQL, Azure Database for PostgreSQL with pgvector, and Azure Managed Redis for caching, streaming, and vector search.

By the end of the course, developers will be able to connect services, orchestrate AI workflows, and build secure, scalable, and observable AI‑driven applications on Azure.

Skills Covered

  • 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 and troubleshoot apps on Azure

Prerequisites

  • Programming experience with languages such as Python, JavaScript, or C#.
  • Basic understanding of Azure services and cloud computing concepts.
  • Familiarity with container concepts.
  • Familiarity with command-line tools including Azure CLI.
  • Familiarity with containerization and Kubernetes fundamentals.
  • Access to Azure Kubernetes Service and related tools for practical exercises.
  • Familiarity with relational databases and SQL fundamentals.
  • Understanding of machine learning concepts including embeddings and similarity search.

Target Audience

This course is designed for developers who build backend and AI‑driven applications on Azure and need practical skills in containerized compute, data services for AI, event‑driven workflows, and application security and monitoring.

Course Curriculum

Module 1: Implement container application hosting on Azure

  • Store and manage containers in Azure Container Registry
  • Deploy containers to Azure App Service

Module 2: Deploy and manage apps on Azure Container Apps

  • Deploy containers to Azure Container Apps
  • Manage containers in Azure Container Apps
  • Scale containers in Azure Container Apps

Module 3: Deploy and monitor applications on Azure Kubernetes Service

  • Deploy applications to Azure Kubernetes Service
  • Configure applications on Azure Kubernetes Service
  • Monitor and troubleshoot applications on Azure Kubernetes Service

Module 4: Develop AI solutions with Azure Cosmos DB for NoSQL

  • Build queries for Azure Cosmos DB for NoSQL
  • Implement vector search on Azure Cosmos DB for NoSQL
  • Optimize query performance for Azure Cosmos DB for NoSQL

Module 5: Develop AI solutions with Azure Database for PostgreSQL

  • Build and query with Azure Database for PostgreSQL
  • Implement vector search with Azure Database for PostgreSQL
  • Optimize vector search in Azure Database for PostgreSQL

Module 6: Enhance AI solutions with Azure Managed Redis

  • Implement data operations in Azure Managed Redis
  • Implement event messaging with Azure Managed Redis
  • Implement vector storage in Azure Managed Redis

Module 7: Integrate backend services for AI solutions

  • Queue and process AI operations with Azure Service Bus
  • Develop event-driven AI workflows with Azure Event Grid
  • Build serverless AI backends with Azure Functions

Module 8: Manage application secrets and configuration for AI solutions

  • Manage application secrets with Azure Key Vault
  • Manage application settings with Azure App Configuration

Module 9: Observe and troubleshoot apps on Azure

  • Instrument an app with OpenTelemetry
  • Analyze app telemetry with logs and metrics

 

Dates & Locations

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July 6, 2026 - July 10, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC
Exam:
RM 374
RM 3500

July 6, 2026 - July 10, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 374
RM 3500

August 3, 2026 - August 7, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC
Exam:
RM 374
RM 3500

August 3, 2026 - August 7, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 374
RM 3500

October 5, 2026 - October 9, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC
Exam:
RM 374
RM 3500

October 5, 2026 - October 9, 2026

Location: Online
Modal: VILT
Availability: TBC
Exam:
RM 374
RM 3500

November 2, 2026 - November 6, 2026

Location: Kuala Lumpur
Modal: ILT
Availability: TBC
Exam:
RM 374
RM 3500

November 2, 2026 - November 6, 2026

Location: Online
Modal: ILT
Availability: TBC
Exam:
RM 374
RM 3500
Trainocate exam and cert

Exam & Certification

Microsoft Certified: Azure AI Cloud Developer Associate

This certification is designed for developers who want to validate their ability to build, integrate, and monitor AI solutions on Azure by using containerized compute, vector-enabled databases, event-driven AI pipelines, serverless functions, secret management, and distributed observability.

Training & Certification Guide

The new Microsoft Certified: Azure AI Cloud Developer Associate validates your knowledge of:

  • building, integrating, and monitoring AI solutions on Azure by using containerized compute
  • vector-enabled databases
  • event-driven AI pipelines
  • serverless functions
  • secret management
  • distributed observability

As a candidate for this Certification, you’re responsible for contributing to all phases of implementing AI solutions on Azure, with an emphasis on back-end services and components. You’re also responsible for supporting all phases of the development lifecycle, including requirements gathering, design, development, deployment, security, and monitoring.

You should be proficient in:

  • Azure SDKs and third-party SDKs used in Azure.
  • Azure data management services.
  • Azure monitoring and troubleshooting.
  • Azure messaging and eventing.
  • Vector databases.
  • Python programming.
  • Implementing containerized applications on Azure.

Skills at a glance

  • Develop containerized solutions on Azure (20–25%)
  • Develop AI solutions by using Azure data management services (25–30%)
  • Connect to and consume Azure services (20–25%)
  • Secure, monitor, troubleshoot Azure solutions (20–25%)

Frequently Asked Questions

AI-200T00 teaches developers how to build scalable AI-powered cloud applications on Microsoft Azure.

This course focuses on creating, deploying, monitoring, and troubleshooting AI solutions using Azure-native cloud services. Learners will work with modern architectures including serverless APIs, event-driven workflows, vector databases, and containerized applications.

Key learning areas:

  • AI cloud application development
  • Azure Functions and serverless APIs
  • Event-driven AI workflows
  • AI observability and monitoring
  • Vector-enabled databases and caching

Pro Tip: Focus on understanding cloud architecture patterns, not just AI models. Scalable infrastructure skills are highly valuable in enterprise AI environments.

This course is designed for developers building AI-powered cloud applications on Azure.

It targets developers who want to create modern AI-native applications using Azure infrastructure, APIs, and event-driven architectures.

Best suited for:

  • Cloud developers
  • Backend developers
  • AI application developers
  • Azure developers transitioning into AI

Pro Tip: Familiarity with APIs, containers, and Azure services will help you maximize the value of this course.

You will learn how to build, integrate, deploy, and monitor AI cloud applications at scale.

The course focuses on practical cloud engineering skills required for modern AI systems running in production environments.

Skills gained:

  • Deploying AI applications with containers
  • Building serverless AI APIs
  • Implementing event-driven AI workflows
  • Managing vector search and AI data services
  • Monitoring and troubleshooting AI workloads

Pro Tip: Learning observability and monitoring is critical. Many AI projects fail because teams cannot properly monitor production systems.

AI-103 focuses on AI apps and agents, while AI-200 focuses on cloud-native AI infrastructure and scalable deployment.

Although both are AI developer courses, they target different layers of the AI stack.

Key differences:

  • AI-103T00:
    • Focus: AI apps, agents, generative AI
    • Role: AI Application Developer
  • AI-200T00:
    • Focus: Cloud-native AI architecture and infrastructure
    • Role: AI Cloud Developer

AI-200 emphasizes distributed systems, observability, event-driven workflows, and cloud scalability.

Pro Tip: Combining AI-103 and AI-200 creates a powerful full-stack AI development skillset.

AI-200 replaces AZ-204 and reflects Microsoft’s shift toward AI-native cloud development.

Microsoft announced that AI-200 replaces AZ-204T00: Developing Solutions for Microsoft Azure, signaling that AI capabilities are becoming a core expectation for cloud developers.

Key evolution:

  • AZ-204 (retiring):
    • Traditional Azure application development
    • APIs, storage, compute, messaging
  • AI-200 (new):
    • AI-native cloud applications
    • Event-driven AI workflows
    • Vector databases and AI observability
    • AI infrastructure and orchestration

This transition reflects how modern cloud applications increasingly incorporate AI services and intelligent workflows.

Pro Tip: If you are pursuing cloud development long-term, AI-native development skills will likely become essential.

Yes, it aligns with the Microsoft Azure AI Cloud Developer Associate certification pathway.

The course validates skills in developing AI-powered cloud applications using Azure-native services, event-driven architectures, and scalable infrastructure.

What it covers:

  • AI cloud application architecture
  • Distributed AI workflows
  • Vector search and AI data services
  • Monitoring and observability

Pro Tip: Focus on hands-on implementation and architecture design. Cloud AI certifications increasingly emphasize real-world operational skills.

It prepares you for emerging cloud-native AI development roles.

As organizations operationalize AI at scale, demand is increasing for developers who can build secure, scalable, and observable AI applications in cloud environments.

Relevant roles:

  • AI Cloud Developer
  • Azure AI Developer
  • Cloud-Native Application Developer
  • Backend AI Engineer
  • AI Platform Developer

Microsoft positions AI-native cloud development as a major direction for future Azure developer roles.

Pro Tip: Cloud developers with AI integration skills are becoming more valuable than traditional application developers.

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