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. Azure OpenAI Service

  Why this is correct

  Azure OpenAI Service is the only option here that provides a managed, pre-built generative language model. It exposes APIs for GPT-4 and other autoregressive transformers that predict the next token in a sequence, allowing the marketing team to turn product keywords into coherent, human-readable descriptions with simple prompt instructions. These models support customization, content filters, and prompt engineering, so they can generate fresh copy immediately without any model training.

• ✗

  B. Azure Computer Vision

  Why it's wrong here

  Azure Computer Vision is an image and video analysis service that uses convolutional neural networks to detect objects, read text via OCR, and generate short auto-generated captions for visual content. It cannot accept a product's keywords and produce extended marketing copy; its text outputs are limited to describing what is present in an image. The service operates on pixels and visual features, not on open-ended language generation.

  When this WOULD be correct

  A question asking which service to use for automatically generating captions or descriptions for images in a product catalog, where the input is an image file and the output is a text description of the image content.

• ✗

  C. Language Understanding (LUIS)

  Why it's wrong here

  Language Understanding (LUIS) is designed for conversational intent recognition and entity extraction, not text generation. When a user sends an utterance, LUIS returns structured JSON containing the intent name and extracted entities—it never composes new natural language output. Its architecture is based on classification models, so it has no way to synthesize a novel product description from keywords.

  When this WOULD be correct

  A question asks: 'Which Azure service should be used to build a conversational bot that can understand user requests to book a flight, extracting the destination and date?' LUIS would be correct for intent and entity recognition in such a scenario.

• ✗

  D. Azure Machine Learning

  Why it's wrong here

  Azure Machine Learning is a robust MLOps platform for orchestrating experimentation, training, and deployment of custom models, but it ships no pre-trained text-generation model out of the box. To generate product descriptions through Azure ML alone, the team would have to source or create a dataset, train or fine-tune a transformer model, set up inference endpoints, and manage scaling—a substantial engineering effort. Because Azure OpenAI Service already offers production-ready generative models, Azure ML is overkill and not the fastest path for this scenario.

  When this WOULD be correct

  Azure Machine Learning would be correct if the question asked for building a custom predictive model to forecast inventory demand based on historical sales data, or if the team needed to train a custom text generation model using their own product description dataset.

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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