Azure Generative AI - Self-Directed Study Track¶

ℹ️ Cross-cert study track, not a single Microsoft certification. Microsoft's GenAI content is split across several certs (AI-900 fundamentals, AI-102 AI Engineer Associate, plus newer AI-3xxx/Copilot specialties). This dir is a focused study guide for Generative AI on Azure (Azure OpenAI, AI Studio, prompt engineering, responsible AI) that complements the cert-specific dirs:

• Foundational coverage: exams/azure/ai-900/
• Engineer-tier coverage: exams/azure/ai-102/

Quick Links¶

• Fact Sheet - Exam logistics and objectives
• Practice Plan - Structured learning path

Study Materials¶

Core Notes¶

• Azure OpenAI Service - GPT, DALL-E, embeddings, Azure OpenAI deployment
• Azure AI Studio & Prompt Engineering - Studio workflows and prompt design
• Responsible AI & Model Deployment - Ethics, governance, deployment patterns

Companion Materials¶

• Azure AI Engineer (AI-102) - The deeper-dive exam after this introduction
• Azure AI Fundamentals (AI-900) - Foundational pre-requisite

Exam Focus Areas¶

The AI-102 exam measures ability to:

1. Plan and Manage an Azure AI Solution (15-20%)
2. Select appropriate AI services
3. Plan for AI solution requirements
4. Create and configure AI service resources

5. Manage AI service security and compliance

6. Implement Computer Vision Solutions (20-25%)

7. Analyze images and videos with Vision services
8. Extract text from images with Form Recognizer
9. Implement custom vision models

10. Integrate vision solutions into applications

11. Implement Natural Language Processing Solutions (20-25%)

12. Analyze text with Language services
13. Process speech with Speech services
14. Translate text and speech with Translator

15. Build and manage conversational AI solutions

16. Implement Knowledge Mining Solutions (15-20%)

17. Create and manage Azure Cognitive Search solutions
18. Implement data source connections and indexing
19. Define and implement skillsets for data enrichment

20. Design and implement search user experiences

21. Implement Generative AI Solutions (15-20%)

22. Use Azure OpenAI Service models
23. Optimize generative AI solutions
24. Implement responsible AI practices
25. Integrate generative AI into applications

Key Azure AI Services¶

Azure OpenAI Service¶

• GPT Models: Text generation and completion
• DALL-E: Image generation from text descriptions
• Codex: Code generation and completion
• Embeddings: Text similarity and semantic search
• ChatGPT: Conversational AI capabilities

Azure Cognitive Services¶

Vision Services¶

• Computer Vision: Image analysis and OCR
• Custom Vision: Custom image classification
• Face: Face detection and recognition
• Form Recognizer: Document analysis and data extraction

Language Services¶

• Text Analytics: Sentiment analysis and key phrases
• Language Understanding (LUIS): Intent and entity recognition
• QnA Maker: Question and answer solutions
• Translator: Text and document translation

Speech Services¶

• Speech to Text: Audio transcription
• Text to Speech: Voice synthesis
• Speech Translation: Real-time speech translation
• Speaker Recognition: Voice identification

Decision Services¶

• Anomaly Detector: Identify unusual patterns
• Content Moderator: Content filtering
• Personalizer: Personalized recommendations

Azure Cognitive Search¶

• AI-powered search: Intelligent information retrieval
• Built-in skills: Pre-built AI enrichment
• Custom skills: Extensible AI pipeline
• Knowledge store: Structured data extraction

Bot Framework¶

• Bot Framework SDK: Multi-platform bot development
• Bot Framework Composer: Visual bot authoring
• Azure Bot Service: Cloud hosting for bots
• Bot Framework Emulator: Local bot testing

Prerequisites & Expectations¶

Required Knowledge¶

• Programming experience: C# or Python
• Azure fundamentals: Understanding of Azure services and concepts
• AI/ML concepts: Basic understanding of artificial intelligence
• REST APIs: Experience with web services and APIs

Technical Skills¶

• Provision and configure Azure AI services
• Process and analyze text, images, and speech
• Implement natural language processing solutions
• Create knowledge mining solutions with Cognitive Search
• Build conversational AI solutions
• Monitor and troubleshoot AI solutions

Success Criteria¶

• Design and implement AI solutions using Azure services
• Integrate AI capabilities into applications
• Configure and manage Azure Cognitive Services
• Build intelligent search solutions with Cognitive Search
• Implement responsible AI practices and governance
• Optimize AI solutions for performance and cost