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Module 10: Analyzers & Scoring - Exercises

This directory contains hands-on exercises for practicing text analysis and scoring profile implementation in Azure AI Search.

Exercise Overview

These exercises are designed to provide practical experience with:

  • Analyzer Configuration: Working with built-in and custom analyzers
  • Text Processing: Understanding tokenization, filtering, and normalization
  • Scoring Profiles: Implementing field weights and scoring functions
  • Performance Optimization: Testing and optimizing analyzer and scoring configurations
  • Real-world Scenarios: Applying concepts to business use cases

Exercise Structure

Each exercise includes:

  • Objective: Clear learning goals
  • Prerequisites: Required knowledge and setup
  • Instructions: Step-by-step implementation guide
  • Validation: Methods to verify correct implementation
  • Extensions: Additional challenges for advanced learners

Exercise List

Beginner Exercises (1-4)

  1. Analyzer Comparison Lab - Compare built-in analyzers
  2. Custom Analyzer Workshop - Build domain-specific analyzers
  3. Basic Scoring Implementation - Create field weight profiles
  4. Autocomplete System - Implement n-gram analyzers

Intermediate Exercises (5-8)

  1. E-commerce Search Optimization - Complete product search system
  2. Multi-language Content Analysis - Handle diverse content
  3. Advanced Scoring Functions - Complex relevance tuning
  4. Performance Testing Framework - Measure and optimize

Advanced Exercises (9-12)

  1. Content Management System - Full-featured search implementation
  2. Location-based Search - Geographic scoring and filtering
  3. A/B Testing Platform - Compare configurations scientifically
  4. Production Deployment - Enterprise-ready implementation

Getting Started

Prerequisites

Before starting these exercises, ensure you have:

  • Completed Module 10 documentation
  • Azure AI Search service (Standard tier or higher)
  • Development environment set up
  • Basic understanding of JSON and REST APIs

Setup Instructions

  1. Clone or download the exercise files
  2. Configure your Azure AI Search service details
  3. Install required dependencies for your chosen language
  4. Validate your setup with the configuration test

Exercise Progression

We recommend completing exercises in order, as later exercises build upon concepts from earlier ones:

Beginner (1-4) → Intermediate (5-8) → Advanced (9-12)

However, you can also focus on specific areas:

  • Analyzer Focus: Exercises 1, 2, 4, 6
  • Scoring Focus: Exercises 3, 5, 7, 10
  • Performance Focus: Exercises 8, 11, 12
  • Real-world Applications: Exercises 5, 9, 10, 12

Exercise Format

Each exercise follows this structure:

📋 Exercise Header

  • Difficulty: Beginner/Intermediate/Advanced
  • Duration: Estimated completion time
  • Skills: Key concepts covered

🎯 Objective

Clear statement of what you'll learn and build

📚 Prerequisites

  • Required knowledge
  • Setup requirements
  • Previous exercises (if applicable)

🛠️ Instructions

Step-by-step implementation guide with: - Code examples - Configuration snippets - Testing procedures

✅ Validation

Methods to verify your implementation: - Expected outputs - Test cases - Performance benchmarks

🚀 Extensions

Optional challenges for deeper learning: - Advanced features - Performance optimizations - Integration scenarios

💡 Solutions

Reference implementations and explanations

Assessment Criteria

Your exercise implementations will be evaluated on:

  1. Correctness (40%): Does the solution work as specified?
  2. Best Practices (25%): Follows Azure AI Search best practices?
  3. Performance (20%): Efficient and optimized implementation?
  4. Documentation (15%): Clear code comments and explanations?

Support Resources

Documentation

Code Samples

Community

Tips for Success

🎯 Focus on Understanding

  • Don't just copy code - understand why it works
  • Experiment with different configurations
  • Test with your own data when possible

🔍 Debug Systematically

  • Use the Analyze API to test text processing
  • Check index schemas carefully
  • Validate configurations before deployment

📊 Measure Performance

  • Baseline performance before optimizing
  • Test with realistic data volumes
  • Monitor both indexing and query performance

🔄 Iterate and Improve

  • Start with simple implementations
  • Add complexity gradually
  • Refactor based on testing results

Completion Certificate

After completing all exercises, you can:

  1. Self-assess using the provided rubrics
  2. Document your implementations and learnings
  3. Share your solutions with the community
  4. Apply these skills to real-world projects

Next Steps

After completing these exercises:

  • Explore Advanced Topics: Vector search, semantic search
  • Build Real Applications: Apply skills to actual projects
  • Join the Community: Share knowledge and learn from others
  • Stay Updated: Follow Azure AI Search updates and new features

These exercises provide hands-on experience with the core concepts of text analysis and scoring in Azure AI Search, preparing you for real-world implementation scenarios.