AI-900 Practice Question: Describe features of generative AI workloads on Azure
An advertising agency wants to generate product images from text prompts. They need the ability to specify the visual style (e.g., photorealistic, oil painting) and also ensure that the generated images are safe for work by blocking inappropriate content. Which Azure OpenAI model and feature should they use?
⚠ Common exam trap
Candidates often confuse text-based models (GPT-4, GPT-3.5) with image generation models, assuming any Azure OpenAI service can handle multimodal tasks, or overlook that DALL-E's built-in content filtering is the specific feature for safety, not a generic moderation add-on.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
DALL-E with built-in content filtering
B is correct because DALL-E is the Azure OpenAI model specifically designed for generating images from text prompts, and it includes built-in content filtering to block inappropriate or unsafe content. This combination directly meets the agency's need to specify visual styles (e.g., photorealistic, oil painting) via prompt engineering while ensuring safety compliance without additional configuration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
GPT-4 with standard content filtering
Why it's wrong here
GPT-4 is an autoregressive transformer designed for language understanding and generation. Although GPT-4 Turbo with Vision can accept image inputs and reason about their content, its output modality is strictly text; it lacks a diffusion decoder or any mechanism to synthesize novel pixel arrays. The standard content filtering in Azure OpenAI is a safety layer that reviews text prompts/completions, not a feature that enables image creation. Therefore, GPT-4 cannot generate product images from a text description.
- ✓
DALL-E with built-in content filtering
Why this is correct
DALL-E is a text-to-image diffusion model hosted in Azure OpenAI, specifically designed to synthesize realistic images from natural language prompts. Its built-in content filtering operates at multiple stages: it filters input prompts for disallowed content, sanitizes objects and attributes during generation, and applies a post-hoc filter to block unsafe or biased imagery. Users can guide the output toward desired product photography styles via detailed prompt engineering (e.g., specifying lighting, angle, background, and branding). This makes DALL-E the correct choice for generating product images from text.
- ✗
GPT-3.5 with custom moderation
Why it's wrong here
GPT-3.5 is a text-in/text-out large language model; its core pre-training objective is predicting the next token in a sequence, not generating image vectors. Adding custom moderation via Azure OpenAI's content filtering service only intercepts and blocks certain textual content—it cannot add an image decoder or a diffusion process to the model. Since GPT-3.5 has no image-generation capability, any customization or moderation layer is irrelevant to producing product images. Thus, this option is technically impossible.
- ✗
Codex with output validation
Why it's wrong here
Codex is a code-generation model derived from GPT-3 and specialized for translating natural language descriptions into source code, not for producing bitmap or vector images directly. While output validation (e.g., syntax checking, unit tests, safe execution) could ensure generated code runs correctly, it does not endow Codex with the ability to generate visual imagery. Even if it produced a program to draw an image, that would be a programmatic workaround rather than AI-driven text-to-image generation, and it is not the Azure OpenAI image generation service. Consequently, Codex does not meet the advertising agency's requirement.
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Azure Machine Learning Studio
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
Key term
Prompt engineering
Prompt engineering is the practice of designing and refining input queries to AI models to get the most accurate, relevant, and useful outputs.
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Written by Johnson Ajibi, MSc IT Security
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