Here is some additional information to help you solidify your knowledge and understanding of the concepts and prep for the AI-900 exam.

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Generative AI vs Predictive ML vs Traditional AI comparison matrix

Aspect	Generative AI	Predictive Machine Learning	Traditional (Rule-Based) AI
Primary Purpose	Generate new content	Predict outcomes or values	Execute predefined rules
Typical Output	Text, images, audio, code, video	Labels, categories, numbers, scores	Yes/No decisions or fixed actions
Creates New Content?	✅ Yes	❌ No	❌ No
Learns From Data?	✅ Yes (large-scale pretraining)	✅ Yes (task-specific training)	❌ No (rules written by humans)
Uses Probabilities?	✅ Yes	✅ Yes	❌ No
Deterministic Output?	❌ No (responses may vary)	⚠️ Usually deterministic	✅ Yes
Handles Unstructured Data	✅ Excellent	⚠️ Limited	❌ Poor
Example Tasks	Chatbots, summarization, image generation, translation	Fraud detection, churn prediction, demand forecasting	Eligibility checks, business rules, workflow automation
Typical Algorithms / Models	Transformers, large language models	Regression, classification, clustering models	If-then rules, decision trees (manual)
Training Data Size	Very large, diverse datasets	Moderate, task-specific datasets	None
Needs Prompts?	✅ Yes	❌ No	❌ No
Adaptable to Many Tasks	✅ High	⚠️ Medium	❌ Low
Common Azure Services	Azure OpenAI Service	Azure Machine Learning	Logic Apps, Power Automate
Example Use Case	Generate a marketing email from a prompt	Predict customer churn probability	Approve a loan if all conditions are met

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Quick Mental Model / One-Line Summaries

Think of it this way:

• Generative AI → “Create something new”
• Predictive ML → “Predict or classify something”
• Traditional AI → “Follow the rules exactly”

Or put another way:

• Generative AI: Produces new content using large pretrained models
• Predictive ML: Uses historical data to predict outcomes
• Traditional AI: Uses human-defined rules to make decisions

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Common AI-900 Trap to Avoid

❌ “Generative AI is just a type of predictive model”

While generative AI uses prediction internally, its goal is content creation, not classification or numeric prediction.

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