This post is a part of the AI-901: Microsoft Azure AI Fundamentals Exam Prep Hub. 
This topic falls under these sections:
Implement AI solutions by using Microsoft Foundry (55–60%)
   --> Implement AI solutions with computer vision and image-generation capabilities by using Foundry
      --> Create new visual outputs by using generative models

Note that there are 10 practice questions (with answers and explanations) for each section to help you solidify your knowledge of the material. Also, there are 2 practice tests with 60 questions each available on the hub below the exam topics section.

Generative AI models are capable of creating entirely new content based on patterns learned during training. One important category of generative AI focuses on producing visual outputs such as images, artwork, diagrams, and design concepts.

For the AI-901 certification exam, candidates should understand the foundational concepts behind creating new visual outputs by using generative AI models through Microsoft Azure AI Foundry and related Azure AI services.

This topic falls under the “Implement AI solutions with computer vision and image-generation capabilities by using Foundry” section of the AI-901 exam objectives.

What Is Generative AI?

Generative AI refers to AI systems capable of creating new content rather than simply analyzing existing data.

Generative AI can produce:

Text
Images
Audio
Video
Code

What Are Generative Image Models?

Generative image models create new visual content from prompts or instructions.

These models can generate:

Artwork
Illustrations
Photorealistic images
Concept designs
Marketing graphics

Example Prompt

“Create an image of a futuristic city at sunset.”

The model generates a new image based on the description.

Azure AI Foundry

Azure AI Foundry provides tools for building and deploying AI-powered applications, including generative AI solutions.

Developers can:

Access generative models
Test prompts
Deploy models
Build AI applications

Image Generation Workflow

A common image-generation workflow includes:

User enters prompt
Application sends prompt to model
Generative model creates image
Application displays generated output

Text-to-Image Generation

Text-to-image models generate images from natural-language prompts.

Example

Prompt

“A golden retriever wearing sunglasses on a beach.”

Result

A newly generated image matching the description.

Image Editing

Some generative models can modify existing images.

Capabilities may include:

Removing objects
Replacing backgrounds
Extending images
Applying artistic styles

Example

Original Image

Photo of a park

Prompt

“Add snow to the scene.”

The model generates an updated version of the image.

Style Transfer

Style transfer applies artistic styles to images.

Example

Prompt

“Make this image look like a watercolor painting.”

The AI transforms the image style.

Inpainting

Inpainting fills missing or selected portions of images.

Example

A damaged image has missing areas that the AI reconstructs.

Outpainting

Outpainting expands images beyond their original boundaries.

Example

A cropped landscape image is extended to show more scenery.

Prompt Engineering

Prompt engineering involves crafting prompts that improve AI-generated results.

Good prompts are:

Clear
Detailed
Specific

Weak Prompt Example

“Create a dog.”

Better Prompt Example

“Create a realistic golden retriever sitting beside a lake during sunset.”

System Prompts

System prompts guide the overall behavior of the AI model.

They may define:

Safety rules
Content restrictions
Tone
Style preferences

Model Parameters

Generative AI models may use parameters that influence output behavior.

Common concepts include:

Creativity/randomness
Response length
Style guidance

For AI-901, conceptual understanding is more important than memorizing exact parameter names.

APIs and Endpoints

Applications communicate with deployed generative models using:

APIs
Endpoints

These allow prompts and images to be processed programmatically.

Authentication

Applications must securely authenticate before using Azure AI services.

Common authentication methods include:

API keys
Azure credentials
Managed identities

User Interface Components

A lightweight image-generation application may include:

Prompt text box
Image upload option
Generate button
Image display area

Real-Time Generation

Some applications generate images interactively in near real time.

This improves user experience and experimentation.

Common Real-World Scenarios

Scenario 1: Marketing Content Creation

Goal

Generate promotional graphics.

Features

Text-to-image generation
Brand-aligned designs
Rapid content creation

Scenario 2: Product Concept Design

Goal

Visualize product ideas.

Features

Prototype generation
Style experimentation
Rapid iteration

Scenario 3: Educational Content

Goal

Generate learning visuals and illustrations.

Features

Diagram generation
Visual storytelling
Accessibility support

Scenario 4: Entertainment and Gaming

Goal

Create concept art and environments.

Features

Character design
Landscape generation
Artistic experimentation

Responsible AI Considerations

Generative image applications should follow Responsible AI principles.

Key considerations include:

Fairness
Privacy
Transparency
Inclusiveness
Accountability
Security

Copyright and Intellectual Property

Organizations should consider:

Ownership rights
Licensing concerns
Use of copyrighted material

Generated content may still raise legal and ethical questions.

Harmful Content Risks

Generative AI systems may create:

Offensive content
Misleading images
Unsafe material

Content filtering and moderation are important safeguards.

Deepfakes

AI-generated images or videos designed to imitate real people are called deepfakes.

Deepfakes can create ethical and security concerns.

Hallucinations

Generative models may produce inaccurate or unrealistic outputs.

These incorrect outputs are called hallucinations.

Bias and Fairness

Generated images may unintentionally reflect societal biases.

Examples include:

Stereotypical portrayals
Uneven representation
Cultural bias

Transparency

Users should understand:

AI generated the image
Outputs may contain inaccuracies
Images may be synthetic rather than real

Error Handling

Applications should handle:

Invalid prompts
Unsupported file types
Network interruptions
Authentication failures
Rate limits

Advantages of Generative Image Models

Benefits include:

Faster content creation
Creative assistance
Rapid prototyping
Automation
Enhanced user engagement

Limitations of Generative Models

Challenges include:

Hallucinations
Bias
Ethical concerns
Copyright uncertainty
Variable output quality

High-Level Workflow

A simplified workflow includes:

User enters prompt
Application sends request
Model generates image
Application displays output

Example High-Level Pseudocode

			
prompt = get_prompt()
image = generate_image(prompt)
display_image(image)

For AI-901, understanding the workflow is more important than memorizing exact syntax.

Important AI-901 Exam Tips

For the exam, remember these key points:

Generative AI creates new content.
Text-to-image models generate images from prompts.
Azure AI Foundry supports generative AI development.
Prompt engineering improves output quality.
APIs and endpoints connect applications to AI services.
Authentication secures access to Azure AI resources.
Deepfakes are synthetic media designed to imitate real people.
Hallucinations are inaccurate AI-generated outputs.
Responsible AI principles apply to generative image systems.
Transparency is important when presenting AI-generated content.

Quick Knowledge Check

Question 1

What does a text-to-image model do?

Answer

Generates images from natural-language prompts.

Question 2

What is prompt engineering?

Answer

Designing prompts to improve AI-generated results.

Question 3

What are deepfakes?

Answer

AI-generated media designed to imitate real people.

Question 4

Why is transparency important in generative AI?

Answer

Users should understand that AI generated the content and that inaccuracies may exist.

Practice Exam Questions

Question 1

What is the PRIMARY purpose of a generative AI model?

A. To create new content based on learned patterns
B. To replace computer hardware
C. To increase internet bandwidth
D. To manage operating systems

Correct Answer

A. To create new content based on learned patterns

Explanation

Generative AI models create new outputs such as images, text, audio, or video using patterns learned during training.

Why the Other Answers Are Incorrect

B. To replace computer hardware

Generative AI is software-based and does not replace hardware.

C. To increase internet bandwidth

AI models do not improve network speeds.

D. To manage operating systems

Operating system management is unrelated to generative AI.

Question 2

What does a text-to-image model do?

A. Generates images from text prompts
B. Converts images into spreadsheets
C. Detects malware in files
D. Compresses image files automatically

Correct Answer

A. Generates images from text prompts

Explanation

Text-to-image models create images based on natural-language descriptions provided by users.

Why the Other Answers Are Incorrect

B. Converts images into spreadsheets

This is unrelated to generative AI.

C. Detects malware in files

This is a cybersecurity task.

D. Compresses image files automatically

Compression is unrelated to image generation.

Question 3

Which Microsoft platform provides tools for building and deploying generative AI applications?

A. Azure AI Foundry
B. Microsoft Paint
C. Windows File Explorer
D. Microsoft Notepad

Correct Answer

A. Azure AI Foundry

Explanation

Azure AI Foundry provides tools for deploying, testing, and managing AI-powered applications.

Why the Other Answers Are Incorrect

B. Microsoft Paint

Paint is a graphics editor, not an AI platform.

C. Windows File Explorer

This is a file management tool.

D. Microsoft Notepad

Notepad is a text editor.

Question 4

What is prompt engineering?

A. Designing prompts to improve AI-generated results
B. Repairing damaged computer hardware
C. Compressing images into smaller files
D. Monitoring internet traffic

Correct Answer

A. Designing prompts to improve AI-generated results

Explanation

Prompt engineering involves creating clear and specific prompts to guide AI systems toward better outputs.

Why the Other Answers Are Incorrect

B. Repairing damaged computer hardware

This is unrelated to AI prompting.

C. Compressing images into smaller files

Compression is unrelated to prompts.

D. Monitoring internet traffic

This is a networking task.

Question 5

Which prompt is MOST likely to generate a detailed image?

A. “Create a dog.”
B. “Generate.”
C. “Create a realistic golden retriever sitting beside a lake during sunset.”
D. “Image.”

Correct Answer

C. “Create a realistic golden retriever sitting beside a lake during sunset.”

Explanation

Detailed prompts generally produce more accurate and useful AI-generated images.

Why the Other Answers Are Incorrect

A. “Create a dog.”

This prompt is too vague.

B. “Generate.”

This provides almost no guidance.

D. “Image.”

This prompt is incomplete and unclear.

Question 6

What is inpainting?

A. Filling or reconstructing parts of an image
B. Converting speech into text
C. Detecting objects in video streams
D. Encrypting image files

Correct Answer

A. Filling or reconstructing parts of an image

Explanation

Inpainting allows AI to fill in missing or selected regions within an image.

Why the Other Answers Are Incorrect

B. Converting speech into text

This is speech recognition.

C. Detecting objects in video streams

This is a computer vision task.

D. Encrypting image files

Encryption is unrelated to inpainting.

Question 7

What are deepfakes?

A. AI-generated media designed to imitate real people
B. Hardware failures in AI systems
C. Encrypted image storage systems
D. High-speed networking protocols

Correct Answer

A. AI-generated media designed to imitate real people

Explanation

Deepfakes use generative AI to create realistic but synthetic media that imitates real individuals.

Why the Other Answers Are Incorrect

B. Hardware failures in AI systems

This is unrelated to generated media.

C. Encrypted image storage systems

This is unrelated to deepfakes.

D. High-speed networking protocols

Networking is unrelated to deepfake technology.

Question 8

How do applications typically communicate with deployed generative AI models?

A. Through APIs and endpoints
B. Through printer drivers
C. Through monitor calibration settings
D. Through USB-only connections

Correct Answer

A. Through APIs and endpoints

Explanation

Applications use APIs and endpoints to send prompts and receive generated outputs from AI services.

Why the Other Answers Are Incorrect

B. Through printer drivers

Printers are unrelated to AI communication.

C. Through monitor calibration settings

This is unrelated to cloud AI services.

D. Through USB-only connections

Cloud AI services use network communication.

Question 9

Which Responsible AI concern is especially important for generative image models?

A. Preventing harmful or misleading content generation
B. Increasing keyboard typing speed
C. Improving spreadsheet formulas
D. Reducing monitor power consumption

Correct Answer

A. Preventing harmful or misleading content generation

Explanation

Generative AI systems can potentially create unsafe, offensive, or misleading content, making moderation and safeguards important.

Why the Other Answers Are Incorrect

B. Increasing keyboard typing speed

This is unrelated to Responsible AI.

C. Improving spreadsheet formulas

This is unrelated to image generation.

D. Reducing monitor power consumption

This is unrelated to AI ethics.

Question 10

What are hallucinations in generative AI systems?

A. Inaccurate or fabricated AI-generated outputs
B. Hardware installation errors
C. Network outages
D. Audio playback failures

Correct Answer

A. Inaccurate or fabricated AI-generated outputs

Explanation

Hallucinations occur when generative AI produces incorrect, unrealistic, or invented outputs.

Why the Other Answers Are Incorrect

B. Hardware installation errors

This is unrelated to AI-generated content.

C. Network outages

This is a connectivity issue.

D. Audio playback failures

This is unrelated to generative image models.

Final Thoughts

Creating new visual outputs by using generative models is an important AI-901 certification topic. Microsoft expects candidates to understand the foundational concepts behind generative image AI, including text-to-image generation, prompt engineering, APIs, deployment, Responsible AI principles, hallucinations, and ethical considerations.

Azure AI Foundry provides powerful tools for building intelligent applications capable of generating creative visual content for business, education, accessibility, and entertainment scenarios.

Go to the AI-901 Exam Prep Hub main page