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.

• ✓

  A. GPT-4 for text and DALL-E for images

  Why this is correct

  GPT-4 is a large language model trained for multi-turn dialogue, enabling the bot to understand conversational context and generate coherent, human-like answers. DALL-E is a generative diffusion model that synthesizes original images from natural-language prompts, which is exactly what a chatbot needs to produce relevant visuals on demand. Together they provide both text generation and image generation, covering the full requirement.

• ✗

  B. GPT-3 for text and Custom Vision for images

  Why it's wrong here

  GPT-3 can generate text, so the conversational half of the pairing is somewhat plausible, but Custom Vision is a supervised image classification and object detection service, not a generative model. Custom Vision learns to assign labeled categories or draw bounding boxes around objects in existing images, and it cannot create a new image from a text description. Therefore, this combination fails the image-generation requirement even though the text side works.

  When this WOULD be correct

  This option would be correct if the question asked for a solution to classify product images and answer FAQs using a pre-trained text model, without requiring generative image creation. For example: 'A company needs to categorize uploaded product photos and answer common customer questions.'

• ✗

  C. BERT for text and OCR for images

  Why it's wrong here

  BERT is an encoder-only transformer pretrained for language understanding tasks such as sentiment analysis, named entity recognition, and extractive question answering, but it has no decoder that can generate free-form text. OCR (optical character recognition) extracts text from images or documents, which is the inverse of image generation—it reads text from images rather than creating images from text. This pairing offers neither conversational text generation nor image generation, making it entirely incompatible with the chatbot requirements.

  When this WOULD be correct

  If the question asked for a system that classifies text sentiment and extracts text from scanned documents, BERT for text classification and OCR for image-to-text would be appropriate.

• ✗

  D. Language Understanding (LUIS) and Face API

  Why it's wrong here

  LUIS is a natural-language understanding service that extracts intents and entities from user utterances, but it does not generate the free-form conversational replies a chatbot needs for customer interaction. Face API detects and analyzes faces in existing photos—returning attributes like age, emotion, and landmarks—but it cannot synthesize or generate images from text. Both services are discriminative analyzers, not generative generators, so this option misses both core capabilities.

  When this WOULD be correct

  This option would be correct for a question asking to build a system that understands customer intents from text (e.g., booking a flight) and analyzes faces for identity verification or emotion detection, without requiring text generation or image creation.

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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