AI-900 Practice Question: Describe features of generative AI workloads on Azure
A creative agency wants to use Azure OpenAI to generate marketing images from text descriptions. They need to ensure that the generated images are appropriate for all audiences by automatically blocking sexually explicit or violent content. Which Azure OpenAI feature should they configure to meet this requirement?
⚠ Common exam trap
Candidates often assume custom training or text-based models are needed, when Azure OpenAI's DALL-E deployment already includes built-in content filtering that directly addresses the requirement.
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
✓
Enable content filtering on the DALL-E deployment
Azure OpenAI's DALL-E deployment includes built-in content filtering that automatically blocks sexually explicit, violent, or otherwise inappropriate images from being generated. This feature is configured at the deployment level and requires no custom model training, making it the simplest and most effective way to meet the requirement for all-audience appropriateness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the GPT-4 model with safety prompts
Why it's wrong here
GPT-4 is a large language model that works with text and code, not images, so it has no capability to generate the visual output the agency needs. Additionally, 'safety prompts' are ad-hoc instructions, not a built-in Azure OpenAI safety mechanism; they can be overridden by prompt injection. This option fundamentally misunderstands both the model's modality and Azure's responsible AI guardrails.
- ✓
Enable content filtering on the DALL-E deployment
Why this is correct
Azure OpenAI's content filtering is a native, built-in safety layer for DALL-E deployments that automatically reviews both the text prompt and the generated image for policy violations, covering categories such as hate, sexual, violence, and self-harm. You can configure severity thresholds and block particular content categories, ensuring the image output meets compliance standards without adding custom code. This is the direct, supported way to address content risk in image generation.
- ✗
Train a custom image classification model to filter outputs
Why it's wrong here
Training a separate image classifier to filter DALL-E outputs is unnecessary because Azure OpenAI already includes content filtering for generated images. Building a custom classifier would require large labeled datasets, additional compute, and ongoing maintenance, while introducing another point of failure and higher latency. It also does not evaluate the text prompt itself for harmful instructions, so it misses a key part of the content safety pipeline.
- ✗
Use the Embeddings model to detect inappropriate content
Why it's wrong here
The Embeddings model is designed to convert text into high-dimensional numerical vectors for similarity search, semantic search, or clustering, and it does not generate images or process image pixels. It lacks the ability to detect violent, hateful, or sexually explicit imagery in DALL-E output because it only understands text representations, not visual content. Using embeddings for content filtering here would be ineffective and outside the model's intended purpose.
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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
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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