Azure AI Engineer Associate (AI-102) Study Guide
🎯 Overview
This comprehensive study guide is designed to help you prepare for the Microsoft Certified: Azure AI Engineer Associate certification (Exam AI-102). The guide covers all exam objectives with practical examples, hands-on labs, and real-world scenarios.
📋 Exam Information
- Exam Code: AI-102
- Exam Title: Designing and Implementing a Microsoft Azure AI Solution
- Duration: 150 minutes
- Question Types: Multiple choice, case studies, drag-and-drop
- Passing Score: 700/1000
- Prerequisites: None (but Azure fundamentals recommended)
🎯 Learning Objectives
By completing this study guide, you will be able to:
- Plan and manage Azure AI solutions (25-30%)
- Implement computer vision solutions (20-25%)
- Implement natural language processing solutions (20-25%)
- Implement knowledge mining and document intelligence solutions (15-20%)
- Implement generative AI solutions (10-15%)
📚 Study Guide Structure
Module 1: Azure AI Services Foundation
Duration: 1 week
Weight: 25-30% of exam
Learning Objectives
- Understand Azure AI services architecture and capabilities
- Learn about Azure AI services pricing, quotas, and limits
- Master authentication and security concepts
- Explore Azure AI services management and monitoring
Key Topics
Azure AI Services Overview
- Computer Vision
- Language Understanding
- Speech Services
- Translator
- Anomaly Detector
- Personalizer
- Content Moderator
- Metrics Advisor
Resource Management
- Resource groups and subscriptions
- Resource provisioning and configuration
- Cost management and optimization
- Quota and limit management
Authentication and Security
- API keys and endpoints
- Azure Active Directory authentication
- Managed identities
- Network security and private endpoints
- Data encryption and compliance
Monitoring and Logging
- Application Insights integration
- Custom metrics and dashboards
- Log analytics and troubleshooting
- Performance monitoring
Hands-on Labs
- Lab 1.1: Create and configure Azure AI services resources
- Lab 1.2: Implement authentication patterns (API keys vs Azure AD)
- Lab 1.3: Set up monitoring and logging with Application Insights
- Lab 1.4: Configure security policies and private endpoints
Study Resources
Module 2: Computer Vision Solutions
Duration: 1 week
Weight: 20-25% of exam
Learning Objectives
- Implement Azure AI Vision services for image analysis
- Build and deploy custom vision models
- Process and analyze images at scale
- Integrate computer vision capabilities into applications
Key Topics
Azure AI Vision Service
- Image analysis and tagging
- Object detection and recognition
- Face detection and recognition
- OCR (Optical Character Recognition)
- Spatial analysis
- Read API for document processing
Custom Vision
- Custom image classification
- Object detection models
- Model training and evaluation
- Model deployment and management
- Performance optimization
Face Service
- Face detection and verification
- Face identification
- Emotion recognition
- Face grouping and similarity
Form Recognizer
- Prebuilt models (receipts, invoices, business cards)
- Custom model training
- Layout analysis
- Table extraction
Hands-on Labs
- Lab 2.1: Image analysis with Azure AI Vision
- Lab 2.2: Custom vision model training and deployment
- Lab 2.3: Face detection and recognition implementation
- Lab 2.4: OCR and document processing
- Lab 2.5: Form Recognizer integration
Study Resources
Module 3: Natural Language Processing Solutions
Duration: 1 week
Weight: 20-25% of exam
Learning Objectives
- Implement Azure AI Language services for text analysis
- Build custom language models and applications
- Develop conversational AI solutions
- Create language understanding applications
Key Topics
Azure AI Language Service
- Text analytics (sentiment, key phrases, entities)
- Language detection
- Named entity recognition
- Personal information detection
- Text summarization
Language Understanding (LUIS)
- Intent recognition
- Entity extraction
- Model training and testing
- Publishing and management
- Integration patterns
QnA Maker
- Knowledge base creation
- Question and answer management
- Active learning
- Multi-turn conversations
- Integration with Bot Framework
Translator Service
- Text translation
- Language detection
- Custom translation models
- Batch translation
- Document translation
Speech Services
- Speech-to-text
- Text-to-speech
- Speech translation
- Custom speech models
- Voice assistants
Hands-on Labs
- Lab 3.1: Text analytics implementation
- Lab 3.2: Custom text classification
- Lab 3.3: LUIS application development
- Lab 3.4: QnA Maker integration
- Lab 3.5: Speech services implementation
Study Resources
Module 4: Knowledge Mining & Document Intelligence
Duration: 1 week
Weight: 15-20% of exam
Learning Objectives
- Implement Azure AI Search solutions
- Build document processing and enrichment pipelines
- Create custom skills and cognitive search capabilities
- Develop knowledge mining applications
Key Topics
Azure AI Search
- Search service fundamentals
- Index creation and management
- Query syntax and optimization
- Scoring profiles and ranking
- Faceted navigation and filters
Cognitive Search
- Skillset definition and configuration
- Built-in cognitive skills
- Custom skills development
- Enrichment pipeline
- Indexer configuration
Document Intelligence
- Prebuilt models
- Custom model training
- Layout analysis
- Table extraction
- Document classification
Knowledge Mining Patterns
- Content extraction and enrichment
- Search and discovery
- Analytics and insights
- Integration with Power BI
Hands-on Labs
- Lab 4.1: Azure AI Search index creation
- Lab 4.2: Cognitive search skillset development
- Lab 4.3: Custom skills implementation
- Lab 4.4: Document intelligence integration
- Lab 4.5: Knowledge mining pipeline
Study Resources
Module 5: Generative AI Solutions
Duration: 1 week
Weight: 10-15% of exam
Learning Objectives
- Implement Azure OpenAI services
- Master prompt engineering techniques
- Build RAG (Retrieval Augmented Generation) applications
- Create AI agents and copilots
Key Topics
Azure OpenAI Service
- GPT models and capabilities
- Embeddings and vector search
- Fine-tuning and customization
- Content filtering and safety
- Usage and quota management
Prompt Engineering
- Prompt design principles
- Few-shot learning
- Chain-of-thought prompting
- Prompt optimization techniques
- Best practices and patterns
RAG Implementation
- Vector databases and embeddings
- Document chunking strategies
- Retrieval optimization
- Generation quality improvement
- Evaluation and monitoring
AI Agents and Copilots
- Agent architecture patterns
- Tool integration
- Memory and context management
- Multi-agent systems
- Human-AI collaboration
Hands-on Labs
- Lab 5.1: Azure OpenAI integration
- Lab 5.2: Prompt engineering exercises
- Lab 5.3: RAG implementation
- Lab 5.4: AI agent development
- Lab 5.5: Content safety configuration
Study Resources
🧪 Assessment Strategy
Practice Exams
- Microsoft Official Practice Test: Available on Microsoft Learn
- Third-party Practice Tests: MeasureUp, Whizlabs
- Self-assessment Quizzes: After each module
Study Milestones
- Week 2: Complete Module 1 assessment (80%+ required)
- Week 4: Complete Module 2-3 assessment (80%+ required)
- Week 6: Complete Module 4-5 assessment (80%+ required)
- Week 8: Final comprehensive practice exam (85%+ required)
Exam Day Preparation
- Review all module summaries
- Complete final practice exam
- Review common exam scenarios
- Prepare exam day checklist
- Plan for technical difficulties
📖 Additional Resources
Official Microsoft Resources
- Microsoft Learn AI-102 Learning Path
- AI-102 Exam Skills Outline
- Microsoft Learn Azure AI Documentation
Community Resources
Books and Publications
- "Azure AI Engineer Associate (AI-102) Study Guide" by Renaldi Gondosubroto
- "Azure AI Services Quick Start Guide" by Microsoft
- "Building AI Applications with Azure" by Microsoft Press
🎯 Exam Tips and Strategies
General Tips
- Read questions carefully - Pay attention to keywords like "best", "most appropriate", "primary"
- Eliminate wrong answers - Use process of elimination
- Manage your time - Allocate time per question (about 1.5 minutes per question)
- Review your answers - Use remaining time to review flagged questions
Technical Tips
- Know the services - Understand capabilities and limitations of each Azure AI service
- Understand integration patterns - Know how services work together
- Practice with real scenarios - Work through case studies and hands-on labs
- Stay updated - Follow Azure AI service updates and new features
Common Exam Scenarios
- Architecture decisions - Choosing the right service for specific requirements
- Integration challenges - Connecting multiple services effectively
- Performance optimization - Improving response times and throughput
- Security implementation - Implementing proper authentication and authorization
- Cost optimization - Choosing cost-effective solutions
📅 Study Schedule Recommendations
8-Week Intensive Program
- Weeks 1-2: Foundation and Computer Vision
- Weeks 3-4: NLP and Knowledge Mining
- Weeks 5-6: Generative AI and Advanced Integration
- Weeks 7-8: Production Deployment and Exam Preparation
12-Week Part-Time Program
- Weeks 1-3: Foundation and Computer Vision
- Weeks 4-6: NLP and Knowledge Mining
- Weeks 7-9: Generative AI and Advanced Integration
- Weeks 10-12: Production Deployment and Exam Preparation
16-Week Comprehensive Program
- Weeks 1-4: Foundation and Computer Vision
- Weeks 5-8: NLP and Knowledge Mining
- Weeks 9-12: Generative AI and Advanced Integration
- Weeks 13-16: Production Deployment and Exam Preparation
This study guide is designed to be comprehensive and practical. Follow the structured approach, complete all hands-on labs, and practice regularly to ensure success on the AI-102 exam.