Introduction
Generative AI models are a class of artificial intelligence systems designed to create new content rather than simply analyze or classify existing data. In the AI-900 exam, Microsoft focuses on conceptual understanding, not implementation details. You are expected to recognize what generative AI models do, how they behave, and what makes them different from traditional machine learning models.
Generative AI underpins many modern Azure AI solutions, including Azure OpenAI Service, and plays a central role in text, image, code, and audio generation workloads.
What Is a Generative AI Model?
A generative AI model learns patterns, structure, and relationships from large datasets and uses that knowledge to generate new, original outputs that resemble the data it was trained on.
Unlike predictive models (which output labels or numeric values), generative models produce:
- Text
- Images
- Code
- Audio
- Synthetic data
Key Features of Generative AI Models (Exam Focus)
1. Content Generation
Generative AI models can create new content rather than selecting from predefined responses.
Examples:
- Writing emails, stories, or summaries
- Generating images from text descriptions
- Producing computer code
- Creating conversational responses
AI-900 cue: If the scenario involves creating something new, it likely involves generative AI.
2. Large Pretrained Models
Generative AI models are typically pretrained on massive datasets containing text, images, or other media.
Key characteristics:
- Trained on diverse, large-scale data
- Capture language structure, context, and semantics
- Can generalize to many tasks without retraining
Examples:
- Large language models (LLMs)
- Multimodal foundation models
3. Prompt-Based Interaction
Generative AI models are commonly controlled using prompts, which are natural language instructions or inputs.
Prompts can:
- Ask questions
- Provide instructions
- Set constraints or styles
- Include examples (few-shot prompting)
Exam tip: Prompts guide how the model responds but do not retrain the model.
4. Probabilistic Output (Non-Deterministic)
Generative AI models produce probabilistic responses, meaning:
- The same prompt can produce different outputs
- Responses are not fixed or guaranteed
- Outputs are generated based on likelihood, not rules
This enables creativity but also requires careful validation.
5. Context Awareness
Generative AI models can use context provided in a conversation or prompt to influence responses.
Examples:
- Remembering earlier parts of a conversation
- Adjusting tone or topic based on prior input
- Generating coherent multi-turn dialogue
This is especially relevant for chat-based AI systems.
6. General-Purpose Capability
Generative AI models are often multi-task by design.
A single model can:
- Answer questions
- Summarize text
- Translate languages
- Generate explanations
- Write code
This contrasts with traditional ML models, which are typically task-specific.
7. Fine-Tuning and Customization
While generative AI models are pretrained, they can be:
- Fine-tuned with domain-specific data
- Prompt-engineered for specific use cases
- Configured with system instructions
For AI-900, it’s important to know customization is possible, not how to implement it.
8. Human-Like Outputs
Generative AI models are designed to produce outputs that appear:
- Natural
- Fluent
- Contextually relevant
- Similar to human-generated content
This is especially true for text and conversational AI.
9. Support for Multimodal Data
Some generative AI models can work across multiple data types, such as:
- Text → Image
- Image → Text
- Text → Code
AI-900 expects recognition of this capability, not technical depth.
10. Need for Responsible AI Controls
Generative AI models require safeguards due to risks such as:
- Hallucinations (incorrect but confident outputs)
- Bias
- Harmful or inappropriate content
Microsoft emphasizes:
- Content filtering
- Responsible AI principles
- Human oversight
Generative AI vs Traditional Machine Learning (High-Yield Comparison)
| Aspect | Traditional ML | Generative AI |
|---|---|---|
| Primary goal | Predict or classify | Create new content |
| Output type | Labels or numbers | Text, images, code, audio |
| Task scope | Narrow, specific | Broad, general-purpose |
| Interaction style | Structured inputs | Natural language prompts |
| Creativity | None | High |
Azure Context (What AI-900 Expects You to Recognize)
Generative AI workloads on Azure are commonly delivered through:
- Azure OpenAI Service
- Integrated Azure AI tooling
- Secure, enterprise-ready AI deployments
You are not expected to know APIs or pricing — only capabilities and use cases.
Common Exam Triggers to Watch For 👀
If a question mentions:
- Writing text
- Creating images
- Generating code
- Conversational responses
- Prompt-based interaction
➡️ Think: Generative AI model
Summary
For the AI-900 exam, generative AI models are defined by their ability to:
- Generate new content
- Respond to prompts
- Operate probabilistically
- Handle multiple tasks
- Produce human-like outputs
- Require responsible AI safeguards
Understanding these features, not implementation details, is the key to scoring well in this exam section.
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