AI-3022: Implement knowledge mining with Azure AI Search

Microsoft is retiring material AI-3022: Implement knowledge mining with Azure AI Search on May 29, 2026. No replacement

Course AI-3022: Implement knowledge mining with Azure AI Search

To earn this Microsoft Applied Skills credential, students must demonstrate the ability to build Azure AI Search solutions, implement custom skillsets, and add skill enrichment to an index to optimize search solutions.

Do you have information locked in structured and unstructured data sources? With this course, you will learn how, with Azure AI Search, you can extract key information from this data and enable applications to search and analyze it.

Level: Intermediate - Role: AI Engineer, Developer, Solutions Architect, Student - Product: Azure, Azure AI Search - Subject: Artificial Intelligence

Course aimed at

This course is aimed at:

• Artificial intelligence engineers
  
• Cloud solution developers
  
• Data architects
  
• Enterprise search professionals
  
• Data analysts working with large volumes of unstructured information
  
• Technical teams wishing to implement intelligent search solutions in their organizations

Objectives of training AI-3022: Azure AI Search

• Create search solutions with Azure AI Search: Learn to configure indexes, manage capacity, and apply filters, sorting, and scoring profiles to improve result relevance
• Develop custom skills: Implement custom skills such as text classification or machine learning models to enrich data during the indexing process
• Create a Knowledge Store: Define projections and store enriched data for later analysis
• Apply advanced search functions: Improve the search experience with semantic analysis, vector search, multiple languages, and proximity sorting
• Index external data: Use Azure Data Factory or the Azure AI Search API to index data from external sources
• Maintain and optimize search solutions: Manage the security, performance, costs, and reliability of implemented solutions
• Implement semantic and vector search: Configure semantic ranking and vector search to improve the accuracy and relevance of results

Course Content AI-3022: Implementing knowledge mining with Azure AI Search

Module 1: Creating an Azure AI Search Solution

• Introduction
• Capacity management
  
• Understanding search components
  
• Understanding the indexing process
  
• Searching an index
  
• Filtering and sorting data
  
• Improving the index
  
• Exercise: Creating a search solution

Module 2: Creating a custom capability for Azure AI Search

• Introduction
  
• Defining the custom skill schema
  
• Incorporating a custom skill
  
• Custom text classification skill
  
• Custom Machine Learning skill
  
• Exercise: Creating a custom skill for Azure AI Search

Module 3: Creating a knowledge store with Azure AI Search

• Introduction
  
• Defining projections
  
• Defining a knowledge store
  
• Exercise: Creating a knowledge store

Module 4: Implement advanced search features in Azure AI Search

• Introduction
  
• Improving document ranking with term prioritization
  
• Improving results relevance by adding scoring profiles
  
• Improving an index by using analyzers and tokenized terms
  
• Improving an index to include multiple languages
  
• Improving the search experience by sorting results by distance from a given landmark
  
• Exercise: Implementing improvements in search results

Module 5: Search data outside the Azure platform in Azure AI Search using Azure Data Factory

• Introduction
  
• Indexing data from external data sources using Azure Data Factory
  
• Indexing data using the Azure AI Search push API
  
• Exercise: Adding to an index using the push API

Module 6: Maintain an Azure AI Search solution

• Introduction
  
• Managing the security of an Azure AI Search solution
  
• Optimizing the performance of an Azure AI Search solution
  
• Managing costs of Azure AI Search solutions
  
• Improving the reliability of an Azure AI Search solution
  
• Monitoring an Azure AI Search solution
  
• Debugging search issues using the Azure Portal
  
• Exercise: Troubleshooting search issues

Module 7: Rerank searches with semantic ranking in Azure AI Search

• Introduction
  
• What is semantic ranking?
  
• Configuring semantic ranking
  
• Exercise: Using semantic ranking on an index

Module 8: Perform vector search and retrieval in Azure AI Search

• Introduction
  
• What is vector search?
  
• Preparing for search
  
• Understanding embedding
  
• Exercise: Using the REST API to execute vector search queries

Prerequisites

To take this training, it is recommended to:

• Be familiar with Microsoft Azure
• Have experience developing applications with C# or Python

Language

• Course: English / Spanish
  

Microsoft Applied Skills

This course is part of Microsoft Applied Skills Credentials.

Earn this Microsoft Applied Skills credential. In this course, you will learn how to build AI applications with Azure Database for PostgreSQL.

Applied Skills: Explore all credentials in one guide