“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Microsoft Official Curriculum
Role-Based Certification PrepTrack: Microsoft Certified: Azure AI Cloud Developer Associate (beta)Official Source: Microsoft Learn
MicrosoftIntermediate
Develop AI cloud solutions on Azure
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.
Duration
5 days
Level
Intermediate
Format
Virtual, On-site, or Hybrid
Language
English
Executive brief for stakeholdersDownload Program Brief (PDF)
Microsoft
App Development, Artificial IntelligenceIT
Azure AI Cloud Developer Associate (beta)
Azure
Software Dev
On this page
Ideal for
DeveloperApp Development, Artificial IntelligenceCertification ReadinessTailored Team Delivery
Audience Profile
Built for these roles
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.
Overview
Executive overview
As a candidate for this Microsoft Certification, you're responsible for contributing to all phases of implementing AI solutions on Azure, with an emphasis on back-end services and components.
Program Outcomes
Capabilities your teams will gain
Develop containerized solutions on Azure
Develop AI solutions by using Azure data management services
Connect to and consume Azure services
Secure, monitor, troubleshoot Azure solutions
Curriculum
Curriculum roadmap
1
Develop containerized solutions on Azure
2
Develop AI solutions by using Azure data management services
3
Connect to and consume Azure services
4
Secure, monitor, troubleshoot Azure solutions
1Module 1
Implement container application hosting on Azure
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Module 1
Implement container application hosting on Azure
Learn how to deploy, configure, and troubleshoot containerized applications on Azure.
- Store and manage containers in Azure Container Registry
- Deploy containers to Azure App Service
2Module 2
Deploy and manage apps on Azure Container Apps
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Module 2
Deploy and manage apps on Azure Container Apps
Learn how to deploy, manage, and scale containerized applications on Azure Container Apps.
- Deploy containers to Azure Container Apps
- Manage containers in Azure Container Apps
- Scale containers in Azure Container Apps
3Module 3
Deploy and monitor applications on Azure Kubernetes Service
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Module 3
Deploy and monitor applications on Azure Kubernetes Service
Learn how to deploy, configure, and troubleshoot apps on Azure Kubernetes Service.
- Deploy applications to Azure Kubernetes Service
- Configure applications on Azure Kubernetes Service
- Monitor and troubleshoot applications on Azure Kubernetes Service
4Module 4
Develop AI solutions with Azure Cosmos DB for NoSQL
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Module 4
Develop AI solutions with Azure Cosmos DB for NoSQL
Learn how to develop AI solutions using Azure Cosmos DB for NoSQL, including vector search and query optimization for RAG pipelines and semantic retrieval.
- 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
5Module 5
Develop AI solutions with Azure Database for PostgreSQL
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Module 5
Develop AI solutions with Azure Database for PostgreSQL
Learn how to develop AI solutions using Azure Database for PostgreSQL, including vector search with pgvector for RAG pipelines and semantic retrieval.
- Build and query with Azure Database for PostgreSQL
- Implement vector search with Azure Database for PostgreSQL
- Optimize vector search in Azure Database for PostgreSQL
6Module 6
Enhance AI solutions with Azure Managed Redis
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Module 6
Enhance AI solutions with Azure Managed Redis
Learn how to use Azure Managed Redis to enhance your AI solutions, including caching strategies, data operations, event messaging, and vector storage.
- Implement data operations in Azure Managed Redis
- Implement event messaging with Azure Managed Redis
- Implement vector storage in Azure Managed Redis
7Module 7
Integrate backend services for AI solutions
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Module 7
Integrate backend services for AI solutions
Learn how to integrate backend services for AI solutions on Azure.
- 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
8Module 8
Manage application secrets and configuration for AI solutions
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Module 8
Manage application secrets and configuration for AI solutions
Learn how to manage application secrets and configuration for AI solutions on Azure using Azure Key Vault and Azure App Configuration.
- Manage application secrets with Azure Key Vault
- Manage application settings with Azure App Configuration
9Module 9
Observe and troubleshoot apps on Azure
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Module 9
Observe and troubleshoot apps on Azure
Learn how to instrument distributed applications with OpenTelemetry and analyze telemetry data with Azure Monitor to observe and troubleshoot AI solutions on Azure.
- Instrument an app with OpenTelemetry
- Analyze app telemetry with logs and metrics
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
Certification readinessImplementation-focused labsPrivate cohort deliveryIntermediate practitioner depth
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Builds current Microsoft credential readiness for Azure AI Cloud Developer Associate (beta) using the official Microsoft Learn skill outline.
- •Align labs to your Microsoft tenant and workload scenarios
- •Add readiness checks and exam preparation reviews
- •Extend delivery with role-specific implementation workshops
Credentials
Certification & official source
- •Microsoft Certified: Azure AI Cloud Developer Associate (beta)
Aligned to the official Microsoft Learn course and learning path for this program.
View Official Microsoft Learn PageResources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
Banking & Finance
Representative Enterprise Banking Team
The focus was not just on tooling knowledge, but on helping teams work from a shared operating model as they adopted a more modern data platform.
- Clearer platform operating model across teams
- Improved confidence in modern data stack adoption
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios
