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AI-200 Learn

Develop AI solutions by using Azure data management services, 30–35% of the exam

Develop AI solutions with Azure Database for PostgreSQL

This learning path guides you through developing AI solutions using Azure Database for PostgreSQL: building a data foundation with schema design, SQL queries, and secure Python integration via Microsoft Entra authentication; implementing vector search with pgvector for embeddings and RAG retrieval patterns; and optimizing vector search performance through tuning, indexing, data layout, scaling, and connection pooling.

Start with Explore Azure Database for PostgreSQL
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Build and query with Azure Database for PostgreSQL

Learn how to use Azure Database for PostgreSQL to build data foundations for AI applications. Design schemas, write efficient queries, and integrate with Python applications using secure authentication.

  1. Explore Azure Database for PostgreSQL
  2. Connect to PostgreSQL
  3. Create and manage schemas
  4. Query data
  5. Integrate SDKs and applications
  6. Exercise - Build an agent tool backend on Azure Database for PostgreSQL

Implement vector search with Azure Database for PostgreSQL

Learn how to implement vector search using the pgvector extension in Azure Database for PostgreSQL. Store embeddings, create vector indexes, and build semantic retrieval patterns for AI applications.

  1. Store and query embeddings with pgvector
  2. Perform fast vector similarity search
  3. Manage index lifecycle and embedding updates
  4. Run vector similarity search for semantic retrieval
  5. Implement retrieval patterns for RAG pipelines
  6. Exercise - Implement vector search on Azure Database for PostgreSQL

Optimize vector search in Azure Database for PostgreSQL

Learn how to optimize vector search performance in Azure Database for PostgreSQL using pgvector. Tune configuration parameters, select and configure vector indexes, design efficient data layouts, scale for high-volume workloads, and implement connection pooling for AI applications.

  1. Tune PostgreSQL for pgvector
  2. Choose and configure vector indexes
  3. Optimize data layout
  4. Scale for high-volume workloads
  5. Configure compute and storage for vector workloads
  6. Connection optimization
  7. Exercise - Optimize vector search performance in Azure Database for PostgreSQL