The answer is Azure AI Vision (formerly known as Computer Vision). This service is the correct choice because it includes a specialized image analysis API that uses deep learning models to detect objects, actions, and scenes within an image, then generates a fluent, human-readable caption describing the image’s contents in natural language. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of which Azure AI service handles visual understanding tasks, often appearing alongside similar services like Custom Vision or Form Recognizer—a common trap is confusing Azure AI Vision with Azure Cognitive Services for Language, but remember that Vision is the only one designed for image-to-text description. To lock this in, use the memory tip: “Vision sees and speaks,” meaning it both analyzes the visual scene and outputs a natural language description.
AI-900 Practice Question: Describe features of computer vision workloads on Azure
This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which Azure AI service can analyze an image and return a description of its contents in natural language?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure AI Vision (Computer Vision)
Azure AI Vision (Computer Vision) includes an image analysis API that can generate a human-readable description of an image's contents. This feature uses deep learning models to identify objects, actions, and scenes, then produces a natural language caption describing the image. The correct answer is B because this is the specific service designed for image understanding and description generation.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Azure AI Language
Why it's wrong here
Azure AI Language processes text — not images. Vision services handle image analysis.
✓
Azure AI Vision (Computer Vision)
Why this is correct
Azure AI Vision can analyze images and generate natural language descriptions, identify objects, and extract text from images.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure AI Speech
Why it's wrong here
Azure AI Speech converts speech to text and text to speech — it doesn't analyze image content.
✗
Azure Bot Service
Why it's wrong here
Bot Service builds conversational agents — not an image analysis service.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Azure AI Language (which handles text) with Azure AI Vision, assuming that 'natural language' output implies a language service, when in fact the image-to-text description is a core feature of the Vision service.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Vision's image description feature uses a convolutional neural network (CNN) to extract visual features, which are then fed into a recurrent neural network (RNN) or transformer-based language model to generate captions. The API returns multiple confidence-scored descriptions, allowing developers to choose the most relevant one. In a real-world scenario, this is used for accessibility tools that describe photos to visually impaired users, or for automated content moderation systems that need to understand image context.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Azure AI Vision (Computer Vision) — Azure AI Vision (Computer Vision) includes an image analysis API that can generate a human-readable description of an image's contents. This feature uses deep learning models to identify objects, actions, and scenes, then produces a natural language caption describing the image. The correct answer is B because this is the specific service designed for image understanding and description generation.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Variation 1. A museum wants to create an application that automatically generates descriptive captions for uploaded photos of artworks. The captions should describe the main subject, scene, and artistic style. Which Azure Computer Vision capability should they use?
medium
A.Optical Character Recognition (OCR)
✓ B.Image Analysis (with description feature)
C.Face API
D.Custom Vision (object detection)
Why B: Option B is correct because the Image Analysis capability in Azure Computer Vision includes a 'description' feature that generates human-readable captions summarizing the main subject, scene, and artistic style of an image. This is achieved through pre-trained deep learning models that analyze visual content and produce natural language descriptions, making it ideal for automatically captioning artwork photos.
Last reviewed: Jun 11, 2026
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