Azure AI Engineer Associate (AI-102) Study Plan¶

10-Week Comprehensive Study Schedule¶

Week 1-2: Azure AI Fundamentals and Planning¶

Week 1: Azure AI Foundation¶

Objective: Understand Azure AI ecosystem and planning

Day 1-2: Azure AI Overview¶

• Study Azure AI services portfolio
• Learn resource provisioning and management
• Understand Azure AI service categories
• Review pricing models and cost optimization
• Hands-on: Create Azure AI resources in portal
• Review Notes: ai-fundamentals.md

Day 3-4: Solution Planning¶

• Study AI solution architecture patterns
• Learn service selection criteria
• Understand capacity planning and scaling
• Study multi-service integration approaches
• Practice: Design AI solution architectures
• Lab: Plan end-to-end AI solution

Day 5-7: Security and Compliance¶

• Study Azure AI security features
• Learn managed identities for AI services
• Understand network security and private endpoints
• Study key vault integration for secrets
• Practice: Configure secure AI service access
• Lab: Implement AI service security

Week 2: Azure OpenAI Service Foundation¶

Objective: Master Azure OpenAI Service basics

Day 1-3: Azure OpenAI Service¶

• Study Azure OpenAI deployment and models
• Learn GPT-3.5, GPT-4, and model selection
• Understand embeddings and semantic search
• Study DALL-E for image generation
• Hands-on: Deploy OpenAI models in Azure
• Review Notes: azure-openai.md

Day 4-5: Prompt Engineering¶

• Master prompt engineering techniques
• Learn few-shot and zero-shot patterns
• Understand system messages and context
• Study prompt templates and best practices
• Practice: Create effective prompts
• Lab: Build prompt library for common tasks

Day 6-7: OpenAI Advanced Features¶

• Study function calling with GPT models
• Learn about RAG patterns with OpenAI
• Understand content filtering and moderation
• Study responsible AI for generative models
• Lab: Implement function calling application
• Lab: Build RAG solution with Azure OpenAI

Week 3-4: Computer Vision Solutions¶

Week 3: Vision Services Core¶

Objective: Master Azure Computer Vision capabilities

Day 1-2: Computer Vision API¶

• Study image analysis capabilities
• Learn OCR and spatial analysis
• Understand vision model versions
• Study image tagging and categorization
• Hands-on: Analyze images with Computer Vision
• Review Notes: computer-vision.md

Day 3-4: Form Recognizer¶

• Study document analysis capabilities
• Learn pre-built models (receipts, invoices, ID cards)
• Understand custom model training
• Study layout and table extraction
• Lab: Extract data from documents
• Lab: Train custom Form Recognizer model

Day 5-7: Custom Vision¶

• Study Custom Vision project types
• Learn image classification and object detection
• Understand training and iteration process
• Study model export and deployment options
• Lab: Build custom image classifier
• Lab: Train object detection model

Week 4: Advanced Vision Features¶

Objective: Implement production vision solutions

Day 1-2: Face API¶

• Study face detection and verification
• Learn face recognition capabilities
• Understand facial attributes analysis
• Study responsible AI for face services
• Lab: Implement face verification
• Lab: Face recognition scenario

Day 3-4: Video Indexer¶

• Study video analysis capabilities
• Learn content moderation for videos
• Understand insights extraction
• Study custom models for Video Indexer
• Lab: Analyze video content
• Lab: Extract video insights

Day 5-7: Vision Integration¶

• Study vision service API integration
• Learn SDKs (Python, C#) for vision services
• Practice error handling and retry logic
• Understand performance optimization
• Lab: Build multi-vision service application
• Lab: Production-ready vision API integration

Week 5-6: Natural Language Processing Solutions¶

Week 5: Language Services Core¶

Objective: Master Azure Language Services

Day 1-2: Text Analytics¶

• Study sentiment analysis capabilities
• Learn key phrase extraction
• Understand entity recognition (NER)
• Study language detection
• Hands-on: Analyze text with Language service
• Review Notes: natural-language.md

Day 3-4: Question Answering¶

• Study question answering service (formerly QnA Maker)
• Learn knowledge base creation and management
• Understand active learning and improvements
• Study multi-turn conversations
• Lab: Build knowledge base from documents
• Lab: Implement Q&A chatbot

Day 5-7: Language Understanding (LUIS)¶

• Study LUIS concepts: intents and entities
• Learn utterance training and patterns
• Understand LUIS application lifecycle
• Study batch testing and evaluation
• Lab: Build LUIS application
• Lab: Train and improve LUIS model

Week 6: Speech and Translation¶

Objective: Implement speech and translation solutions

Day 1-2: Speech Services¶

• Study Speech-to-Text capabilities
• Learn Text-to-Speech and SSML
• Understand custom speech models
• Study speech translation
• Lab: Implement speech recognition
• Lab: Build text-to-speech application
• Review Notes: speech-services.md

Day 3-4: Translator Service¶

• Study text translation capabilities
• Learn document translation
• Understand custom translation models
• Study transliteration features
• Lab: Multi-language translation
• Lab: Document translation pipeline

Day 5-7: Conversational Language Understanding¶

• Study conversational AI patterns
• Learn orchestration workflows
• Understand multi-intent scenarios
• Practice: Design conversation flows
• Lab: Build conversational application
• Lab: Implement orchestration

Week 7-8: Knowledge Mining and Bot Solutions¶

Week 7: Azure Cognitive Search¶

Objective: Master AI-powered search solutions

Day 1-2: Cognitive Search Fundamentals¶

• Study index creation and schema design
• Learn data source connections
• Understand indexers and scheduling
• Study search query syntax
• Lab: Create search index
• Review Notes: cognitive-search.md

Day 3-4: AI Enrichment¶

• Study built-in cognitive skills
• Learn skillset creation and configuration
• Understand knowledge store
• Study custom skills development
• Lab: Build enrichment pipeline
• Lab: Create custom skill

Day 5-7: Search Optimization¶

• Study relevance tuning and scoring profiles
• Learn faceting and filtering
• Understand semantic search
• Study search analytics and monitoring
• Lab: Implement semantic search
• Lab: Optimize search relevance

Week 8: Bot Framework¶

Objective: Build conversational AI solutions

Day 1-3: Bot Framework SDK¶

• Study bot architecture and components
• Learn dialogs and conversation flow
• Understand state management
• Study multi-channel deployment
• Lab: Build bot with SDK (Python or C#)
• Review Notes: bot-framework.md

Day 4-5: Bot Framework Composer¶

• Study visual bot authoring
• Learn adaptive dialogs
• Understand Language Generation (LG)
• Study bot testing and debugging
• Lab: Build bot with Composer
• Lab: Implement complex conversation flow

Day 6-7: Bot Integration¶

• Study LUIS integration with bots
• Learn QnA Maker integration
• Understand Azure OpenAI integration
• Practice: Multi-service bot solution
• Lab: Build intelligent bot with NLU
• Lab: Deploy bot to channels

Week 9: Responsible AI and Monitoring¶

Day 1-2: Responsible AI Practices¶

• Study fairness and bias mitigation
• Learn transparency and explainability
• Understand privacy and security for AI
• Study inclusive design principles
• Review Notes: responsible-ai.md
• Practice: Evaluate AI solution for fairness

Day 3-4: Content Safety¶

• Study Azure Content Safety service
• Learn content moderation techniques
• Understand harm categories and severity
• Study custom content policies
• Lab: Implement content moderation
• Lab: Configure content safety policies

Day 5-7: Monitoring and Diagnostics¶

• Study Azure Monitor for AI services
• Learn Application Insights integration
• Understand logging and diagnostics
• Study performance optimization
• Lab: Implement comprehensive monitoring
• Lab: Set up alerts and diagnostics

Week 10: Integration, Practice, and Exam Prep¶

Day 1-2: End-to-End Solutions¶

• Study multi-service integration patterns
• Learn API management for AI services
• Practice: Design complete AI solutions
• Understand DevOps for AI applications
• Lab: Build integrated AI application
• Lab: Implement CI/CD for AI solution

Day 3-4: Practice Exam 1¶

• Take first comprehensive practice exam
• Review all incorrect answers thoroughly
• Identify knowledge gaps by domain
• Create targeted study plan for gaps
• Target: Score 70%+ on first attempt

Day 5-6: Practice Exam 2 and Review¶

• Deep dive into weak areas
• Complete hands-on labs for gaps
• Take second practice exam
• Compare performance with first exam
• Target: Score 80%+ consistently

Day 7: Final Preparation¶

• Review key concepts and service capabilities
• Practice service selection scenarios
• Review responsible AI principles
• Light review only, avoid cramming
• Prepare for exam day logistics

Daily Study Routine (2-3 hours/day)¶

Weekday Schedule¶

1. 45 minutes: Study documentation and concepts
2. 75 minutes: Hands-on labs and coding
3. 30 minutes: Practice questions
4. 15 minutes: Review and note-taking

Weekend Extended Sessions (4-6 hours)¶

1. 2 hours: Complex hands-on projects
2. 2 hours: Practice exams and review
3. 1-2 hours: Weak area remediation

Hands-on Lab Requirements¶

Essential Labs by Week¶

1. Week 1-2: Azure OpenAI deployment and prompt engineering
2. Week 3-4: Vision services and custom models
3. Week 5-6: NLP services and speech solutions
4. Week 7-8: Cognitive Search and bot development
5. Week 9: Monitoring and responsible AI implementation
6. Week 10: End-to-end integrated solutions

Real-world Projects¶

• Document processing pipeline with Form Recognizer
• Intelligent search application with Cognitive Search
• Multi-language chatbot with translation
• Content moderation system with Computer Vision
• RAG application with Azure OpenAI

Study Resources by Priority¶

Primary Resources¶

1. Microsoft Learn: AI-102 official learning path
2. Azure AI Documentation: Service-specific guides
3. GitHub Samples: Official Azure AI samples repository
4. Hands-on Labs: Azure portal and SDK practice

Secondary Resources¶

1. Pluralsight: AI-102 course track
2. Cloud Academy: Azure AI courses
3. YouTube: Azure AI content and tutorials
4. Practice Exams: MeasureUp, Whizlabs

Success Metrics and Milestones¶

Weekly Milestones¶

• Week 2: Deploy and use Azure OpenAI models
• Week 4: Build custom vision models
• Week 6: Implement NLP and speech solutions
• Week 8: Create intelligent search and bots
• Week 9: Apply responsible AI practices
• Week 10: Pass practice exams at 85%+

Exam Readiness Indicators¶

• Consistently score 85%+ on practice exams
• Complete labs without documentation reference
• Explain service selection rationale clearly
• Troubleshoot common issues independently

Final Exam Tips¶

Question Strategy¶

• Read scenario requirements carefully
• Identify key constraints (cost, performance, etc.)
• Consider responsible AI implications
• Think about integration complexity

Common Exam Scenarios¶

• Service selection for specific use cases
• Custom model vs pre-built service decisions
• Multi-service integration architecture
• Performance optimization approaches
• Security and compliance requirements
• Monitoring and troubleshooting

Time Management¶

• Allocate 180 minutes for 40-60 questions
• Spend ~3 minutes per question average
• Flag uncertain questions for review
• Save time for case study scenarios
• Review all answers if time permits