Develop AI solutions by using Azure data management services, 30–35% of the exam
Develop AI solutions with Azure Cosmos DB for NoSQL
This learning path guides you through developing AI solutions using Azure Cosmos DB for NoSQL: building a data foundation with the resource model, SDK integration, CRUD, and SQL queries; implementing vector search for embeddings, similarity queries, hybrid search, and change-feed sync; and optimizing query performance via indexing and consistency levels.
Build queries for Azure Cosmos DB for NoSQL
Learn how to connect to Azure Cosmos DB for NoSQL using the SDK, perform data operations on items, and write efficient SQL queries to retrieve document data for AI applications.
Implement vector search on Azure Cosmos DB for NoSQL
Learn how to store vector embeddings, execute similarity queries using the VectorDistance function, combine vector search with metadata filters and hybrid search, and use the change feed to keep embeddings synchronized.
- Store and retrieve embeddings in Azure Cosmos DB
- Execute vector similarity queries for semantic search
- Combine vector similarity results with metadata filtering
- Use the change feed to trigger embedding refresh
- Implement a change feed processor with a lease container
- Exercise - Build a semantic search application with Azure Cosmos DB for NoSQL
Optimize query performance for Azure Cosmos DB for NoSQL
Learn how to optimize query performance by analyzing query patterns, configuring range and composite indexes, selecting vector index types, and choosing consistency levels that balance freshness with cost efficiency.