Microsoft Azure AI Fundamentals (AI-900) Quick Reference Guide

This quick reference guide provides concise information about key concepts, services, and terminology covered in the AI-900 exam. Use it for last-minute review and to reinforce your understanding of core concepts.

Exam Domains at a Glance

Domain Weightage Key Focus
AI Workloads & Considerations 15-20% AI workload types, responsible AI principles
Machine Learning Fundamentals 15-20% ML techniques, concepts, Azure ML capabilities
Computer Vision 15-20% Vision solution types, Azure vision services
Natural Language Processing 15-20% NLP workloads, Azure language services
Generative AI 20-25% Gen AI features, Azure OpenAI, AI Foundry

Key Azure AI Services Memory Map

AI & Machine Learning

  • Azure Machine Learning: End-to-end ML platform
    • Automated ML: Automates algorithm selection and hyperparameter tuning
    • ML Designer: Visual interface for creating ML models
    • ML Studio: Web portal for ML projects

Computer Vision

  • Azure AI Vision: Image analysis, OCR, spatial analysis
  • Azure Custom Vision: Custom image classification and object detection
  • Azure AI Face: Face detection, recognition, and analysis
  • Azure AI Document Intelligence: Extract data from documents (formerly Form Recognizer)

Natural Language Processing

  • Azure AI Language:
    • Text analysis (sentiment, key phrases, entities)
    • Language understanding
    • Question answering
    • Conversational language understanding
  • Azure AI Speech:
    • Speech-to-text
    • Text-to-speech
    • Speech translation
    • Speaker recognition
  • Azure AI Translator: Text translation

Generative AI

  • Azure OpenAI Service:
    • GPT models (text generation)
    • DALL-E (image generation)
    • Embeddings (vector representations)
  • Azure AI Foundry:
    • Model catalog
    • Model customization
    • Deployment and monitoring

AI Workload Types - Quick Reference

Computer Vision Workloads

  • Image Classification: “What is this image?”
  • Object Detection: “What objects are in this image and where?”
  • Semantic Segmentation: “Which pixel belongs to which object?”
  • OCR: “What text is in this image?”
  • Face Analysis: “Who is in this image and what are their characteristics?”

NLP Workloads

  • Sentiment Analysis: “Is this text positive, negative, or neutral?”
  • Key Phrase Extraction: “What are the important phrases in this text?”
  • Named Entity Recognition: “What people, places, organizations are mentioned?”
  • Language Detection: “What language is this text written in?”
  • Translation: “Convert this text to another language”
  • Speech Recognition: “Convert speech to text”
  • Speech Synthesis: “Convert text to speech”

Document Processing

  • Form Recognition: Extract data from forms
  • Receipt Analysis: Extract data from receipts
  • Invoice Processing: Extract data from invoices
  • ID Document Analysis: Extract data from IDs

Generative AI

  • Text Generation: Create human-like text
  • Image Generation: Create images from text descriptions
  • Code Generation: Create code from natural language
  • Conversational AI: Create chatbots and assistants

Machine Learning Concepts - Quick Reference

ML Types

  • Supervised Learning: Training with labeled data
    • Classification: Predict categories
    • Regression: Predict numeric values
  • Unsupervised Learning: Finding patterns in unlabeled data
    • Clustering: Group similar items
    • Dimensionality Reduction: Reduce feature space
  • Deep Learning: Neural networks with multiple layers
    • CNNs: For images
    • RNNs: For sequences
    • Transformers: For text and more

ML Workflow

  1. Data Collection: Gather relevant data
  2. Data Preparation: Clean, transform, feature engineering
  3. Model Training: Fit model to training data
  4. Model Evaluation: Test on validation data
  5. Model Deployment: Deploy to production
  6. Model Monitoring: Track performance over time

ML Terminology

  • Features: Input variables
  • Labels: Output variables (what you’re predicting)
  • Training Data: Data used to train the model
  • Validation Data: Data used to tune hyperparameters
  • Test Data: Data used to evaluate final model
  • Overfitting: Model performs well on training data but poorly on new data
  • Underfitting: Model fails to capture the underlying pattern

Responsible AI Principles - Mnemonic: “FAIR-PT”

  • Fairness: AI systems should treat all people fairly
  • Accountability: Take responsibility for how AI systems operate
  • Inclusiveness: Design AI systems that work for everyone
  • Reliability & Safety: Build systems that perform reliably and safely
  • Privacy & Security: Protect user data and privacy
  • Transparency: Make AI systems understandable

Common ML Algorithms - Quick Reference

Classification Algorithms

  • Logistic Regression: Simple, interpretable
  • Decision Trees: Hierarchical decisions
  • Random Forest: Ensemble of decision trees
  • Support Vector Machines: Find optimal boundary
  • Neural Networks: Multi-layer networks

Regression Algorithms

  • Linear Regression: Simple, interpretable
  • Decision Forest Regression: Ensemble of trees
  • Neural Network Regression: Multi-layer networks

Clustering Algorithms

  • K-means: Partition into k clusters
  • Hierarchical Clustering: Build cluster hierarchy
  • DBSCAN: Density-based clustering

Azure Machine Learning Components

Compute Options

  • Compute Instances: Development workstations
  • Compute Clusters: Scalable training clusters
  • Inference Clusters: For model deployment
  • Attached Compute: Link existing resources

Data Storage

  • Datastores: Connections to storage services
  • Datasets: References to specific data

Experiment Tracking

  • Runs: Individual training sessions
  • Metrics: Performance measurements
  • Logs: Detailed output

Model Management

  • Registration: Store models in registry
  • Versioning: Track model versions
  • Deployment: Deploy as web services
  • Monitoring: Track model performance

Computer Vision Concepts

Image Analysis Techniques

  • Feature Extraction: Identify key features
  • Transfer Learning: Reuse pre-trained models
  • Data Augmentation: Create variations of training images

Evaluation Metrics

  • Accuracy: Overall correctness
  • Precision: Exactness of positive predictions
  • Recall: Completeness of positive predictions
  • F1-Score: Harmonic mean of precision and recall
  • IoU: Intersection over Union for object detection
  • mAP: Mean Average Precision for object detection

NLP Concepts

Text Processing Techniques

  • Tokenization: Split text into tokens
  • Stemming/Lemmatization: Reduce words to base form
  • Stop Word Removal: Remove common words
  • Vectorization: Convert text to numeric form
  • Embeddings: Represent words as vectors

Speech Processing

  • Acoustic Modeling: Map audio to phonemes
  • Language Modeling: Predict word sequences
  • SSML: Speech Synthesis Markup Language

Generative AI Concepts

Model Types

  • Autoregressive Models: Generate one token at a time
  • Diffusion Models: Gradually denoise random patterns
  • GANs: Generator and discriminator networks

Techniques

  • Prompt Engineering: Craft effective prompts
  • Fine-tuning: Adapt pre-trained models
  • Few-shot Learning: Learn from few examples
  • Zero-shot Learning: Perform tasks without specific training

Evaluation

  • Human Evaluation: Human judgment of quality
  • Automated Metrics: BLEU, ROUGE, etc.
  • Alignment: Adherence to human values and preferences

Exam Tips

General Tips

  • Read questions carefully - look for keywords
  • Eliminate obviously wrong answers first
  • Watch for “NOT” or “EXCEPT” in questions
  • Manage time - 45 minutes for 40-60 questions

Question Types

  • Multiple Choice: Select one correct answer
  • Multiple Selection: Select all that apply
  • Case Studies: Questions based on scenarios
  • Drag and Drop: Match items to categories

Key Areas to Focus

  • Azure service capabilities and use cases
  • Responsible AI principles
  • ML concepts and terminology
  • Differences between AI workload types
  • Azure OpenAI Service features

Last-Minute Review

  • Review Azure service names and capabilities
  • Memorize responsible AI principles
  • Understand ML types and scenarios
  • Know the differences between vision, NLP, and generative AI workloads
  • Familiarize yourself with Azure AI Foundry and OpenAI Service

Good luck on your exam!