The answer is Image Captioning. This Azure Computer Vision capability is the correct choice because it generates human-readable, sentence-level descriptions of image content, directly fulfilling the requirement to produce alternative text for accessibility. Unlike object detection or optical character recognition, which only identify specific elements or text, Image Captioning synthesizes a complete narrative describing the scene, objects, and actions, making it ideal for screen readers used by visually impaired users. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of how different Computer Vision features map to real-world accessibility needs; a common trap is confusing Image Captioning with object detection or tagging, which output labels rather than descriptive sentences. To remember this, think of the mnemonic “Caption = Complete Alt Text,” where the goal is a full, flowing description that a screen reader can vocalize naturally, not just a list of tags.
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.
A social media platform wants to automatically generate alternative text descriptions for images posted by users to improve accessibility for visually impaired users. Which Azure Computer Vision capability should be used?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Image Captioning
Image Captioning is the correct capability because it generates human-readable descriptions of image content, which directly meets the requirement to produce alternative text for accessibility. Unlike other options, it synthesizes a complete sentence describing the scene, objects, and actions, making it ideal for screen readers.
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.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts printed or handwritten text from images, but does not generate descriptive captions of the scene.
✓
Image Captioning
Why this is correct
Image Captioning automatically generates a natural language description of an image, making it suitable for alt-text generation.
Related concept
Read the scenario before looking for a memorised answer.
✗
Object Detection
Why it's wrong here
Object Detection identifies and locates specific objects in an image but does not produce a comprehensive textual description.
✗
Face Detection
Why it's wrong here
Face Detection finds human faces in images but does not generate descriptive text for accessibility.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Object Detection (which only lists objects) with Image Captioning (which generates a full description), leading them to choose C because they think identifying objects is sufficient for accessibility, but screen readers need natural language descriptions, not just object labels.
Detailed technical explanation
How to think about this question
Azure Computer Vision's Image Captioning uses a neural network combining a convolutional neural network (CNN) for visual feature extraction and a recurrent neural network (RNN) or transformer for language generation. It outputs a caption with a confidence score, and the service can generate multiple captions ranked by probability. In practice, this is used by platforms like Microsoft Seeing AI to describe photos in real-time for visually impaired users.
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: Image Captioning — Image Captioning is the correct capability because it generates human-readable descriptions of image content, which directly meets the requirement to produce alternative text for accessibility. Unlike other options, it synthesizes a complete sentence describing the scene, objects, and actions, making it ideal for screen readers.
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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Question Discussion
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