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Purpose of this document
This study guide should help you understand what to expect on the exam and includes a summary of the topics the exam might cover and links to additional resources. The information and materials in this document should help you focus your studies as you prepare for the exam.
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About the exam
Some exams are localized into other languages, and those are updated approximately eight weeks after the English version is updated. If the exam isn't available in your preferred language, you can request an additional 30 minutes to complete the exam.
Note
The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.
Note
Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.
Skills measured
Audience profile
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. 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%)
Skills measured
NOTE: The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.
NOTE: Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.
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
Study resources
We recommend that you train and get hands-on experience before you take the exam. We offer self-study options and classroom training as well as links to documentation, community sites, and videos.
| Study resources | Links to learning and documentation |
|---|---|
| Get trained | Choose from self-paced learning paths and modules or take an instructor-led course |
| Find documentation | Azure documentation Container Registry Container Instances App Service Azure Functions Azure Cosmos DB Blob Storage Microsoft Entra ID Key Vault Azure Cache for Redis API Apps API Management Event Hubs Event Grid Service Bus Messaging Queue Storage |
| Ask a question | Microsoft Q&A | Microsoft Docs |
| Get community support | Azure Community Support |
| Follow Microsoft Learn | Microsoft Learn - Microsoft Tech Community |
| Find a video | Exam Readiness Zone Azure Fridays Browse other Microsoft Learn shows |