Building a Knowledge Store with Azure AI Search
Build Azure AI Search knowledge store with AI enrichment, projections, and analytics for book catalog data.
Lab overview
Azure AI Search is Microsoft's cloud-based search-as-a-service solution that brings enterprise-grade search capabilities to applications and data analytics workflows. Beyond traditional keyword search, it offers AI-powered enrichment that can extract insights, detect languages, identify key phrases, and transform unstructured content into structured, searchable data. A powerful feature of Azure AI Search is the knowledge store, which persists AI-enriched data to Azure Storage in multiple formats, enabling organizations to build data pipelines, create analytics dashboards, and train machine learning models without repeatedly processing the same content.
In this lab, you will build a complete knowledge store solution using Azure AI Search to enrich book catalog data with AI-powered insights. You'll learn how to configure the enrichment pipeline, define knowledge store projections, and persist enriched data in multiple formats for analytics and downstream processing.
Objectives
Upon completion of this intermediate level lab, you will be able to:
- Configure data sources and skillsets with built-in AI capabilities
- Define knowledge store projections in table and object formats
- Create and execute indexers to populate knowledge stores with enriched data
- Explore and analyze enriched data stored in Azure Storage
- Understand the differences between projection types for various analytical use cases
Who is this lab for?
This lab is designed for:
- Data Engineers building data enrichment pipelines with Azure AI services
- Cloud Solutions Architects designing search and analytics solutions on Azure
- AI/ML Engineers preparing enriched datasets for machine learning workflows
- Business Intelligence Developers creating data sources for analytics and reporting tools
- Azure Developers implementing cognitive search capabilities in applications
- Data Scientists working with AI-enriched data for analysis and modeling
Verified against your live environment
An automated validation engine inspects your actual resources and configurations as you work. Completion means the task was performed — not multiple choice, real-world proficiency.
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Related reading
Environment
Every lab includes
- Real environment, pre-credentialed
- Automated checks on every step
- Isolated sandbox, auto cleanup
- AI-recommended next steps
Lab curriculum
- 01
Logging into Azure Account using Azure Portal
- 02
Uploading Source Data to Azure Storage
1 automated check
- 03
Configuring Data Source and Skillset with Knowledge Store Projections
2 automated checks
- 04
Creating Search Index and Running the Indexer
2 automated checks
- 05
Exploring and Analyzing Knowledge Store Output
Skills validated
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