AI, AI-900, Artificial Intelligence (AI), Generative AI, Microsoft Certification

Identify Common Scenarios for Generative AI (AI-900 Exam Prep)

Overview

In the AI-900: Microsoft Azure AI Fundamentals exam, generative AI represents a significant and growing focus area. This topic assesses your ability to recognize when generative AI is the appropriate solution and how it differs from traditional AI and predictive machine learning.

Generative AI models are designed to create new content—such as text, images, audio, or code—based on patterns learned from large datasets and guided by user prompts.

This article explains common real-world scenarios where generative AI is used, how those scenarios appear on the AI-900 exam, and how they map to Azure services.

What Makes a Scenario “Generative AI”?

A workload is a generative AI scenario when:

  • The output is newly generated content, not just a prediction or classification
  • The model responds to natural language prompts or instructions
  • The output can vary creatively, even for similar inputs

If the task is to predict, classify, or extract, it is not generative AI. If the task is to create, compose, or generate, it is.

Common Generative AI Scenarios (AI-900 Focus)

1. Text Generation

Scenario examples:

  • Writing emails, reports, or marketing copy
  • Drafting blog posts or documentation
  • Generating summaries from bullet points

Why this is generative AI: The model creates original text based on a prompt rather than selecting from predefined responses.

2. Conversational AI and Chatbots

Scenario examples:

  • AI-powered customer support chatbots
  • Virtual assistants that answer open-ended questions
  • Knowledge assistants that explain concepts conversationally

Why this is generative AI: Responses are dynamically generated and context-aware, rather than rule-based or scripted.

3. Text Summarization

Scenario examples:

  • Summarizing long documents
  • Creating executive summaries
  • Condensing meeting transcripts

Why this is generative AI: The model produces a new, concise version of the original content while preserving meaning.

4. Translation and Language Transformation

Scenario examples:

  • Translating text between languages
  • Rewriting text to be simpler or more formal
  • Paraphrasing content

Why this is generative AI: The output text is newly generated rather than extracted or classified.

5. Code Generation and Assistance

Scenario examples:

  • Generating code from natural language descriptions
  • Explaining existing code
  • Refactoring or optimizing code snippets

Why this is generative AI: The model creates original source code based on intent expressed in a prompt.

6. Image Generation

Scenario examples:

  • Creating images from text prompts
  • Generating artwork or design concepts
  • Producing visual content for marketing

Why this is generative AI: The model synthesizes entirely new images rather than identifying objects in existing ones.

7. Audio and Speech Generation

Scenario examples:

  • Converting text into natural-sounding speech
  • Generating voiceovers
  • Creating spoken responses for virtual assistants

Why this is generative AI: The audio output is generated dynamically from text input.

Azure Services Commonly Used for Generative AI

For the AI-900 exam, generative AI scenarios are most commonly associated with:

  • Azure OpenAI Service
    • Large language models (LLMs)
    • Text, code, and image generation
    • Conversational AI

Other Azure services (such as Azure AI Speech or Language) may support generative capabilities, but Azure OpenAI Service is the primary service to associate with generative AI workloads.

Generative AI vs Other AI Approaches (Quick Contrast)

Task TypeAI Approach
Predict a value or categoryPredictive Machine Learning
Follow predefined rulesTraditional AI
Create new text, images, or codeGenerative AI

How This Appears on the AI-900 Exam

On the exam, generative AI scenarios are typically described using words such as:

  • Generate
  • Create
  • Write
  • Summarize
  • Compose
  • Respond conversationally

If the question emphasizes creative or open-ended output, generative AI is likely the correct choice.

Key Takeaways for Exam Day

  • Generative AI is about creation, not prediction
  • Outputs are flexible and context-aware
  • Azure OpenAI Service is the primary Azure service for generative AI
  • If the output did not previously exist, generative AI is likely the answer

Go to the Practice Exam Questions for this topic.

Go to the AI-900 Exam Prep Hub main page.

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