AI-900 Practice Question: Describe features of generative AI workloads on Azure
A creative agency wants to use Azure OpenAI to generate unique images for social media campaigns based on text descriptions. Which Azure OpenAI model should they use for this purpose?
⚠ Common exam trap
Many exam-takers confuse GPT-4's general-purpose AI capabilities with multimodal generation, assuming it can handle images because it can process text and code, but GPT-4 is not designed for image creation.
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 3
DALL-E 3 is the correct choice because it is the Azure OpenAI model specifically designed for generating images from natural language text descriptions. It uses a diffusion-based architecture to create high-quality, unique visuals that align with the provided prompts, making it ideal for creative social media campaigns.
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
Why it's wrong here
GPT-4 is an autoregressive transformer-based language model trained on vast text and code corpora. While some GPT-4 variants can accept image inputs to reason about visual content, the model's output modality is strictly text or code — it never generates pixel-based images. Therefore, it cannot produce unique visual artwork from a prompt, making it incorrect for this creative agency's need.
- ✓
DALL-E 3
Why this is correct
DALL-E 3 is a text-to-image generative model that uses a diffusion-based architecture to synthesize high-resolution, photorealistic images and artwork from natural language prompts. It understands complex descriptions and renders unique visual compositions with accurate object placement and style. This makes it the ideal Azure OpenAI service for creating unique images on demand.
- ✗
Codex
Why it's wrong here
Codex is a fine-tuned descendant of GPT-3 specifically optimized for code generation, completion, and natural-language-to-code translation, as seen in GitHub Copilot. Its training focuses on programming languages and APIs, and its output is executable code, not visual media. Consequently, it lacks any image-generation capability and would be irrelevant to image creation tasks.
- ✗
Whisper
Why it's wrong here
Whisper is an automatic speech recognition (ASR) model trained to transcribe and translate audio across many languages. It operates on log-Mel spectrograms derived from audio waveforms, and its output is text transcripts or translations, never images. Using Whisper for visual art generation would be a fundamental modality mismatch.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.