AI-900 Practice Question: Describe features of generative AI workloads on Azure

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-3.5

  Why it's wrong here

  GPT-3.5 is a large language model optimized for natural language understanding, conversation, and text generation, with no image synthesis capability. Its architecture is transformer-based but operates exclusively on text tokens, outputting strings of text rather than pixel data. While GPT-3.5 can describe an image in words or propose a prompt for an image model, it cannot itself produce a visual output, making it unsuitable for the marketing team's need for original images.

  When this WOULD be correct

  If the question asked for generating marketing copy, summarizing text, or answering questions based on text input, GPT-3.5 would be the correct choice.

• ✓

  DALL-E

  Why this is correct

  DALL-E is a generative AI model available in Azure OpenAI Service, purpose-built for synthesizing original images from natural language text prompts. Unlike text-only models, DALL-E employs a diffusion-based architecture that creates high-fidelity visuals while iteratively aligning them to the described subject, style, and composition. This allows a marketing team to rapidly prototype logos, ad artwork, or social media graphics without needing graphic design software.

• ✗

  Codex

  Why it's wrong here

  Codex, also from OpenAI, is a model fine-tuned for translating natural language instructions into executable code in languages like Python and JavaScript. Its output is source code, not images; although a code snippet could theoretically invoke a drawing library to render a graphic, Codex itself does not synthesize visual content from a prompt. For true text-to-image generation, a dedicated model like DALL-E is required, not a code-generation model.

  When this WOULD be correct

  A question asking which Azure OpenAI model can generate code from natural language prompts, such as 'Which model can convert a description of a function into Python code?' would have Codex as the correct answer.

• ✗

  Azure Speech-to-Text

  Why it's wrong here

  Azure Speech-to-Text is a speech recognition service that converts spoken audio streams into time-stamped text transcripts, used for tasks like meeting captioning and voice commands. It is part of Azure Cognitive Services' speech category and works entirely in the audio-to-text modality, with no neural pathway for generating visuals. Consequently, it has no role in producing pictures, illustrations, or any other image content for marketing campaigns.

  When this WOULD be correct

  If the question were about transcribing customer call recordings into text for analysis, or converting meeting audio into written notes, Azure Speech-to-Text would be the correct choice.

Option-by-option analysis

Why each answer is right or wrong

Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.

Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”

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