Azure AI Cloud Developer
AI-200 follows on from AZ-204 for developers who build AI solutions on Azure: hosting containers, storing embeddings and running vector search in Cosmos DB, PostgreSQL and Redis, connecting services with messaging and functions, and keeping it all secure and observable.
Microsoft Certified: Azure AI Cloud Developer Associate · for developers who build the back end of AI solutions on Azure, in Python
- Level
- Associate
- Exam time
- 120 minutes
- Passing score
- 700 of 1000
- Microsoft Learn during the exam
- Allowed
- Skills outline
- April 15, 2026
01 · Start here
Study guide
Walks you through all 27 topics, start to finish. Skip what you know, get tested where you are not sure, learn the rest.
02 · Explore
Personal feed
An endless mix of study cards. Most choices adapt to what you view and answer, with some random topics to discover new areas.
03 · Look up
Study cards
27 short cards, one per objective. What it is, when to choose it, and where the exam tries to trip you.
04 · Practice
Practice questions
147 questions by topic. Instant feedback, with the reason behind every option and the Microsoft Learn source.
05 · Simulate
Practice exam
50 questions in 120 minutes, in the official domain proportions. Flags, review screen, score per domain.
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Skills measured
What the exam covers
The domains and objectives as Microsoft lists them in the official study guide, version of April 15, 2026. The percentage is the share of the exam.
1 Develop containerized solutions on Azure
20-25%Implement container application hosting
PracticeImplement container-orchestrated solutions
Practice- Deploy applications to Azure Container Apps, including environment configuration and revision management 5 q
- Implement event-driven scaling by using Kubernetes Event‑driven Autoscaling (KEDA) in Container Apps 5 q
- Deploy and manage applications to Azure Kubernetes Service (AKS) by using manifest files 5 q
- Monitor and troubleshoot solutions on AKS and Container Apps by inspecting logs, events, and end-to-end connectivity 5 q
2 Develop AI solutions by using Azure data management services
25-30%Develop AI solutions by using Azure Cosmos DB for NoSQL
Practice- Connect to Azure Cosmos DB for NoSQL by using the SDK and run queries 5 q
- Optimize query performance and Request Units (RUs) consumption by using indexing policies and consistency levels 5 q
- Store and retrieve embeddings and execute vector similarity search for semantic retrieval 5 q
- Implement a change feed processor to detect and handle new or updated items 5 q
Develop AI solutions by using Azure Database for PostgreSQL
Practice- Connect and query Azure Database for PostgreSQL by using SDKs 5 q
- Model schemas and implement indexing strategies, including designing tables and choosing appropriate data types 5 q
- Implement indexing strategies, including optimizing query latency and reducing pgvector compute overhead 5 q
- Configure compute, memory, and storage resources to support vector workloads 5 q
- Run vector similarity search, including storing embeddings, semantic retrieval, and implementing retrieval-augmented generation (RAG) patterns by using metadata filter 5 q
- Implement connection optimization to improve throughput and minimize latency 5 q