Azure AI Fundamentals (AI-900) Study Plan¶

5-Week Study Schedule¶

Week 1: AI Fundamentals and Concepts¶

Objective: Understand AI, ML, and responsible AI principles

Daily Schedule (1-2 hours/day)¶

• Monday: Introduction to AI and ML
• Study: AI vs ML vs Deep Learning concepts
• Watch: Microsoft Learn - AI fundamentals introduction

• Practice: Identify AI scenarios in daily life

• Tuesday: Machine Learning fundamentals

• Study: Supervised, unsupervised, reinforcement learning
• Practice: Classify ML scenarios (regression, classification, clustering)

• Exercise: Understand training vs validation vs test data

• Wednesday: Responsible AI principles

• Study: Fairness, reliability, privacy, inclusiveness, transparency, accountability
• Case studies: Real-world responsible AI examples

• Discussion: Ethical AI considerations

• Thursday: AI workload types

• Study: ML, computer vision, NLP, knowledge mining, document intelligence
• Practice: Identify appropriate workload types for scenarios

• Review: Week 1 materials

• Friday: Deep learning basics

• Study: Neural networks, deep learning applications
• Practice: Understand when deep learning is appropriate

• Quiz: AI concepts and principles

• Weekend: Hands-on exploration

• Explore: Azure AI services in portal
• Try: Simple AI demos and quickstarts
• Review: AI terminology and concepts

Week 2: Azure Machine Learning¶

Objective: Learn Azure ML capabilities and automated ML

Daily Schedule (1-2 hours/day)¶

• Monday: Azure Machine Learning overview
• Study: Azure ML workspace, compute, datastores
• Lab: Create Azure ML workspace

• Practice: Navigate Azure ML studio

• Tuesday: Automated Machine Learning

• Study: AutoML capabilities and use cases
• Lab: Create AutoML experiment for classification

• Practice: Configure AutoML settings

• Wednesday: Azure ML Designer

• Study: Drag-and-drop ML pipeline creation
• Lab: Build simple ML pipeline with designer

• Practice: Understand designer components

• Thursday: Data and compute management

• Study: Datasets, datastores, compute targets
• Practice: Data ingestion and preparation scenarios

• Lab: Configure compute instances and clusters

• Friday: Model deployment and management

• Study: Model registration, deployment options
• Practice: Real-time vs batch inference scenarios

• Review: Week 2 materials

• Weekend: ML practice

• Complete: Additional Azure ML labs
• Practice tests: ML section
• Hands-on: Try different ML scenarios

Week 3: Computer Vision Services¶

Objective: Master Azure computer vision capabilities

Daily Schedule (1-2 hours/day)¶

• Monday: Azure AI Vision service
• Study: Image analysis, OCR, spatial analysis
• Lab: Analyze images with Computer Vision API

• Practice: OCR scenarios and use cases

• Tuesday: Custom Vision service

• Study: Custom image classification and object detection
• Lab: Train custom image classifier

• Practice: When to use custom vs pre-built models

• Wednesday: Face API and analysis

• Study: Face detection, recognition, emotion analysis
• Lab: Implement face detection application

• Practice: Face API scenarios and limitations

• Thursday: Form Recognizer and Document Intelligence

• Study: Pre-built and custom models
• Lab: Extract data from forms and documents

• Practice: Document processing scenarios

• Friday: Video analysis

• Study: Video Indexer capabilities
• Demo: Analyze video content

• Review: Week 3 materials

• Weekend: Computer vision practice

• Hands-on: Multiple computer vision labs
• Practice tests: Computer vision section
• Portfolio: Create demo applications

Week 4: Natural Language Processing and Speech¶

Objective: Understand Azure NLP and speech services

Daily Schedule (1-2 hours/day)¶

• Monday: Language Understanding (LUIS)
• Study: Intents, entities, utterances
• Lab: Build simple LUIS app

• Practice: Design conversational AI scenarios

• Tuesday: Text Analytics service

• Study: Sentiment analysis, key phrase extraction, entity recognition
• Lab: Analyze text sentiment and entities

• Practice: Text analysis use cases

• Wednesday: Speech services

• Study: Speech-to-text, text-to-speech, speech translation
• Lab: Implement speech recognition

• Practice: Voice-enabled application scenarios

• Thursday: Language translation

• Study: Translator Text API capabilities
• Lab: Build translation application

• Practice: Multi-language scenarios

• Friday: QnA Maker and conversational AI

• Study: Knowledge base creation and management
• Lab: Create QnA bot

• Review: Week 4 materials

• Weekend: NLP integration

• Build: Multi-service NLP application
• Practice tests: NLP section
• Integration: Combine multiple language services

Week 5: Advanced AI Services and Exam Preparation¶

Objective: Cover remaining topics and prepare for exam

Daily Schedule (2-3 hours/day)¶

• Monday: Azure OpenAI Service
• Study: GPT models, completions, embeddings
• Demo: OpenAI service capabilities (if available)

• Practice: Generative AI scenarios

• Tuesday: Azure Cognitive Search

• Study: Knowledge mining, indexing, search
• Lab: Create search solution

• Practice: Search and knowledge mining scenarios

• Wednesday: Bot Framework and Power Virtual Agents

• Study: Bot development and deployment
• Practice: Conversational AI platform selection

• Review: Integration scenarios

• Thursday: Practice test and review

• Take: Comprehensive practice test
• Review: Incorrect answers and knowledge gaps

• Study: Identified weak areas

• Friday: Final preparation

• Review: All notes and key concepts
• Practice: Service selection scenarios

• Prepare: Exam day logistics

• Weekend: Exam readiness

• Final practice test
• Confidence building exercises
• Last-minute review

Hands-on Lab Progression¶

Week 1 Labs¶

• Explore Azure AI services in portal
• Try cognitive services demos
• Create free Azure account

Week 2 Labs¶

• Create Azure ML workspace
• Run AutoML classification experiment
• Build pipeline with ML Designer
• Deploy simple model

Week 3 Labs¶

• Analyze images with Computer Vision
• Train Custom Vision classifier
• Implement face detection
• Extract data with Form Recognizer

Week 4 Labs¶

• Build LUIS application
• Analyze text sentiment
• Implement speech recognition
• Create translation app
• Build QnA Maker bot

Week 5 Labs¶

• Create cognitive search solution
• Build comprehensive AI application
• Practice exam scenarios

Key Study Resources¶

Official Microsoft Resources¶

• Microsoft Learn: AI fundamentals learning path
• Azure AI documentation: Service-specific guides
• Azure AI demos: Hands-on experience tools
• Cognitive Services: API documentation and samples

Practice Materials¶

• Microsoft Official Practice Test
• Hands-on labs: Azure AI services quickstarts
• GitHub samples: AI application examples
• Azure AI gallery: Pre-built solutions

Additional Resources¶

• AI Business School: Case studies and strategies
• Azure Architecture Center: AI solution patterns
• Microsoft AI principles: Responsible AI guidelines
• Community forums: Azure AI discussions

Study Tips¶

Technical Preparation¶

• Hands-on Focus: Use Azure AI services extensively
• API Understanding: Know capabilities, not implementation details
• Service Selection: Practice choosing right service for scenarios
• Integration Knowledge: Understand how services work together

Exam Strategy¶

• Scenario-Based: Focus on business use cases
• Service Capabilities: Know what each service can and cannot do
• Responsible AI: Understand ethical considerations
• Pricing Awareness: Basic understanding of cost factors

Common Exam Topics¶

Service Identification¶

• When to use Computer Vision vs Custom Vision
• Text Analytics vs Language Understanding
• Speech-to-Text vs Text-to-Speech scenarios
• Pre-built vs custom models

Responsible AI¶

• Bias detection and mitigation
• Privacy and security considerations
• Transparency and explainability
• Fairness in AI systems

Technical Concepts¶

• Training vs inference
• Supervised vs unsupervised learning
• Classification vs regression
• Confidence scores and thresholds

Success Metrics¶

• Practice Test Score: 80%+ consistently
• Hands-on Confidence: Comfortable with major AI services
• Scenario Recognition: Can identify appropriate services quickly
• Concept Understanding: Explain AI concepts clearly

Emergency 3-Week Plan¶

Week 1: AI concepts + Azure ML basics Week 2: Computer Vision + NLP services Week 3: Practice tests + hands-on review

Focus on Microsoft Learn path and hands-on labs with less theoretical depth.