Azure AI Engineer Associate (AI-102) Training Plan
🎯 Program Overview
This comprehensive 8-week training program is designed to prepare you for the Microsoft Certified: Azure AI Engineer Associate certification (Exam AI-102). The program combines theoretical knowledge with hands-on practical experience through labs, projects, and assessments.
📊 Program Structure
Program Duration: 8 Weeks
- Total Hours: 80-100 hours
- Weekly Commitment: 10-12 hours
- Format: Self-paced with structured milestones
- Assessment: Weekly quizzes + final comprehensive exam
📅 Weekly Training Schedule
Week 1: Azure AI Services Foundation
Focus: Understanding Azure AI ecosystem and foundational concepts
Day 1-2: Azure AI Services Overview
- Learning Objectives:
- Understand Azure AI services architecture
- Learn about service capabilities and use cases
- Explore pricing models and quotas
- Activities:
- Read Azure AI services documentation
- Complete Microsoft Learn modules
- Create Azure account and explore portal
- Time Investment: 4 hours
- Deliverables: Service comparison matrix
Day 3-4: Resource Management & Security
- Learning Objectives:
- Master resource provisioning and management
- Understand authentication and security patterns
- Learn about monitoring and logging
- Activities:
- Lab 1.1: Create and configure Azure AI services
- Lab 1.2: Implement authentication patterns
- Lab 1.3: Set up monitoring and logging
- Time Investment: 4 hours
- Deliverables: Resource management documentation
Day 5-7: Hands-on Practice & Assessment
- Learning Objectives:
- Apply knowledge through practical exercises
- Identify knowledge gaps
- Prepare for next module
- Activities:
- Lab 1.4: Configure security policies
- Complete Module 1 assessment
- Review and remediation
- Time Investment: 4 hours
- Deliverables: Module 1 completion certificate
Week 2: Computer Vision Solutions
Focus: Building computer vision applications with Azure AI Vision
Day 1-2: Azure AI Vision Fundamentals
- Learning Objectives:
- Understand computer vision capabilities
- Learn about image analysis and processing
- Explore prebuilt models and APIs
- Activities:
- Study Azure AI Vision documentation
- Complete Microsoft Learn computer vision modules
- Explore sample applications
- Time Investment: 4 hours
- Deliverables: Computer vision use case analysis
Day 3-4: Custom Vision & Face Services
- Learning Objectives:
- Build and train custom vision models
- Implement face detection and recognition
- Understand model deployment and management
- Activities:
- Lab 2.1: Image analysis implementation
- Lab 2.2: Custom vision model training
- Lab 2.3: Face detection and recognition
- Time Investment: 4 hours
- Deliverables: Custom vision model project
Day 5-7: Advanced Computer Vision & Assessment
- Learning Objectives:
- Implement OCR and document processing
- Work with Form Recognizer
- Complete module assessment
- Activities:
- Lab 2.4: OCR implementation
- Lab 2.5: Form Recognizer integration
- Complete Module 2 assessment
- Time Investment: 4 hours
- Deliverables: Computer vision solution portfolio
Week 3: Natural Language Processing Solutions
Focus: Building NLP applications with Azure AI Language services
Day 1-2: Text Analytics & Language Understanding
- Learning Objectives:
- Implement text analytics services
- Build custom text classification models
- Understand language detection and processing
- Activities:
- Study Azure AI Language documentation
- Complete Microsoft Learn NLP modules
- Lab 3.1: Text analytics implementation
- Time Investment: 4 hours
- Deliverables: NLP use case documentation
Day 3-4: LUIS & QnA Maker
- Learning Objectives:
- Build Language Understanding (LUIS) applications
- Create QnA Maker knowledge bases
- Implement conversational AI solutions
- Activities:
- Lab 3.2: Custom text classification
- Lab 3.3: LUIS application development
- Lab 3.4: QnA Maker integration
- Time Investment: 4 hours
- Deliverables: Conversational AI application
Day 5-7: Speech Services & Assessment
- Learning Objectives:
- Implement speech-to-text and text-to-speech
- Build voice-enabled applications
- Complete module assessment
- Activities:
- Lab 3.5: Speech services implementation
- Complete Module 3 assessment
- Review and remediation
- Time Investment: 4 hours
- Deliverables: Speech-enabled application
Week 4: Knowledge Mining & Document Intelligence
Focus: Building search and document processing solutions
Day 1-2: Azure AI Search Fundamentals
- Learning Objectives:
- Understand search service architecture
- Learn about indexing and querying
- Explore search optimization techniques
- Activities:
- Study Azure AI Search documentation
- Complete Microsoft Learn search modules
- Lab 4.1: Search index creation
- Time Investment: 4 hours
- Deliverables: Search strategy document
Day 3-4: Cognitive Search & Document Intelligence
- Learning Objectives:
- Build cognitive search solutions
- Implement document intelligence
- Create custom skills and enrichment
- Activities:
- Lab 4.2: Cognitive search skillset development
- Lab 4.3: Custom skills implementation
- Lab 4.4: Document intelligence integration
- Time Investment: 4 hours
- Deliverables: Knowledge mining solution
Day 5-7: Advanced Patterns & Assessment
- Learning Objectives:
- Implement knowledge mining patterns
- Build end-to-end document processing
- Complete module assessment
- Activities:
- Lab 4.5: Knowledge mining pipeline
- Complete Module 4 assessment
- Review and remediation
- Time Investment: 4 hours
- Deliverables: Complete knowledge mining application
Week 5: Generative AI Solutions
Focus: Building generative AI applications with Azure OpenAI
Day 1-2: Azure OpenAI Fundamentals
- Learning Objectives:
- Understand Azure OpenAI service capabilities
- Learn about GPT models and embeddings
- Explore content safety and filtering
- Activities:
- Study Azure OpenAI documentation
- Complete Microsoft Learn OpenAI modules
- Lab 5.1: Azure OpenAI integration
- Time Investment: 4 hours
- Deliverables: OpenAI capabilities analysis
Day 3-4: Prompt Engineering & RAG
- Learning Objectives:
- Master prompt engineering techniques
- Implement RAG (Retrieval Augmented Generation)
- Build context-aware applications
- Activities:
- Lab 5.2: Prompt engineering exercises
- Lab 5.3: RAG implementation
- Lab 5.4: AI agent development
- Time Investment: 4 hours
- Deliverables: RAG application
Day 5-7: AI Agents & Assessment
- Learning Objectives:
- Build AI agents and copilots
- Implement content safety measures
- Complete module assessment
- Activities:
- Lab 5.5: Content safety configuration
- Complete Module 5 assessment
- Review and remediation
- Time Investment: 4 hours
- Deliverables: AI agent application
Week 6: Advanced Integration Patterns
Focus: Integrating multiple Azure AI services and advanced architectures
Day 1-2: Multi-Service Integration
- Learning Objectives:
- Design multi-service architectures
- Implement service orchestration
- Handle cross-service data flow
- Activities:
- Study integration patterns documentation
- Lab 6.1: Multi-service integration project
- Lab 6.2: Event-driven AI workflows
- Time Investment: 4 hours
- Deliverables: Integration architecture design
Day 3-4: Performance & Scalability
- Learning Objectives:
- Optimize AI service performance
- Implement caching strategies
- Handle high-volume scenarios
- Activities:
- Lab 6.3: Performance optimization
- Lab 6.4: Error handling implementation
- Lab 6.5: API management setup
- Time Investment: 4 hours
- Deliverables: Performance optimization report
Day 5-7: Advanced Scenarios & Assessment
- Learning Objectives:
- Handle complex business scenarios
- Implement advanced patterns
- Complete module assessment
- Activities:
- Complete advanced integration project
- Complete Module 6 assessment
- Review and remediation
- Time Investment: 4 hours
- Deliverables: Advanced integration solution
Week 7: Production Deployment & Monitoring
Focus: Deploying and managing AI solutions in production
Day 1-2: Deployment Strategies
- Learning Objectives:
- Design deployment architectures
- Implement CI/CD pipelines
- Handle environment management
- Activities:
- Study deployment best practices
- Lab 7.1: CI/CD pipeline setup
- Lab 7.2: Production deployment
- Time Investment: 4 hours
- Deliverables: Deployment strategy document
Day 3-4: Monitoring & Management
- Learning Objectives:
- Implement comprehensive monitoring
- Set up alerting and dashboards
- Manage model lifecycle
- Activities:
- Lab 7.3: Monitoring dashboard creation
- Lab 7.4: Model versioning implementation
- Lab 7.5: Security audit and compliance
- Time Investment: 4 hours
- Deliverables: Monitoring and management solution
Day 5-7: Production Readiness & Assessment
- Learning Objectives:
- Ensure production readiness
- Implement security and compliance
- Complete module assessment
- Activities:
- Complete production readiness checklist
- Complete Module 7 assessment
- Review and remediation
- Time Investment: 4 hours
- Deliverables: Production-ready AI solution
Week 8: Exam Preparation & Practice
Focus: Final exam preparation and practice
Day 1-2: Comprehensive Review
- Learning Objectives:
- Review all exam objectives
- Identify knowledge gaps
- Practice with sample questions
- Activities:
- Complete comprehensive review
- Take practice exams
- Identify weak areas
- Time Investment: 4 hours
- Deliverables: Knowledge gap analysis
Day 3-4: Practice Exams & Scenarios
- Learning Objectives:
- Practice with exam-style questions
- Work through case studies
- Improve time management
- Activities:
- Complete multiple practice exams
- Work through case study scenarios
- Practice time management
- Time Investment: 4 hours
- Deliverables: Practice exam results
Day 5-7: Final Preparation & Exam
- Learning Objectives:
- Final review and preparation
- Take the actual exam
- Celebrate success!
- Activities:
- Final review of weak areas
- Complete final practice exam
- Schedule and take AI-102 exam
- Time Investment: 4 hours
- Deliverables: AI-102 certification!
🎯 Learning Milestones
Week 2 Milestone: Foundation Complete
- Target: Complete Modules 1-2
- Assessment: 80%+ on module assessments
- Deliverable: Computer vision application portfolio
Week 4 Milestone: Core Skills Complete
- Target: Complete Modules 3-4
- Assessment: 80%+ on module assessments
- Deliverable: NLP and search application portfolio
Week 6 Milestone: Advanced Skills Complete
- Target: Complete Modules 5-6
- Assessment: 80%+ on module assessments
- Deliverable: Generative AI and integration solution
Week 8 Milestone: Certification Ready
- Target: Complete all modules and exam preparation
- Assessment: 85%+ on final practice exam
- Deliverable: AI-102 certification
📊 Assessment Strategy
Weekly Assessments
- Format: Multiple choice and hands-on exercises
- Duration: 30-45 minutes
- Passing Score: 80%
- Retake Policy: Unlimited retakes within the week
Module Assessments
- Format: Comprehensive exam covering module objectives
- Duration: 60-90 minutes
- Passing Score: 80%
- Retake Policy: Up to 2 retakes per module
Final Practice Exam
- Format: Full-length practice exam (150 minutes)
- Questions: 50-60 questions covering all objectives
- Passing Score: 85%
- Retake Policy: Up to 3 attempts
🛠️ Hands-on Lab Requirements
Lab Environment Setup
- Azure Subscription: Free tier or pay-as-you-go
- Development Tools: Visual Studio Code, Azure CLI, PowerShell
- Sample Data: Provided datasets for each lab
- Estimated Cost: $50-100 for the entire program
Lab Completion Requirements
- All Labs: Must be completed to pass each module
- Documentation: Each lab requires documentation
- Code Repository: Maintain GitHub repository for all code
- Screenshots: Capture key results and configurations
📚 Study Resources
Primary Resources
- Microsoft Learn: Official learning paths and modules
- Azure Documentation: Comprehensive service documentation
- GitHub Samples: Code samples and tutorials
- Azure Portal: Hands-on practice environment
Secondary Resources
- Books: Azure AI Engineer Associate study guide
- Videos: Microsoft Learn video content
- Community: Azure AI community forums
- Practice Tests: Third-party practice exams
🎯 Success Metrics
Knowledge Metrics
- Module Assessments: 80%+ average score
- Practice Exams: 85%+ on final practice exam
- Hands-on Labs: 100% completion rate
- Documentation: Complete and accurate
Skill Metrics
- Code Quality: Clean, well-documented code
- Architecture: Sound architectural decisions
- Problem Solving: Ability to troubleshoot issues
- Integration: Successful multi-service integration
Certification Metrics
- Exam Score: 700+ on AI-102 exam
- Certification: Microsoft Certified: Azure AI Engineer Associate
- Portfolio: Complete portfolio of AI solutions
- Confidence: Ready for real-world AI projects
📅 Alternative Schedules
12-Week Part-Time Program
- Weekly Commitment: 6-8 hours
- Schedule: 2-3 days per week
- Intensity: Moderate pace with more review time
- Target Audience: Working professionals
16-Week Comprehensive Program
- Weekly Commitment: 5-6 hours
- Schedule: 2-3 days per week
- Intensity: Slower pace with extensive practice
- Target Audience: Beginners or those wanting deep understanding
4-Week Intensive Program
- Weekly Commitment: 20-25 hours
- Schedule: 5-6 days per week
- Intensity: Fast-paced with focused learning
- Target Audience: Experienced developers with Azure background
🎉 Program Completion
Upon Successful Completion
- Certification: Microsoft Certified: Azure AI Engineer Associate
- Portfolio: Complete portfolio of AI solutions
- Skills: Advanced Azure AI development skills
- Confidence: Ready for AI engineering roles
Next Steps
- Advanced Certifications: Azure Solutions Architect, Azure DevOps Engineer
- Specializations: Focus on specific AI domains
- Career Development: Apply skills in real-world projects
- Community: Share knowledge and mentor others
This training plan is designed to be flexible and adaptable to your learning style and schedule. Adjust the pace as needed while maintaining the core learning objectives and assessment requirements.