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 a textual description for each user-uploaded image to assist visually impaired users. Which prebuilt Azure Computer Vision feature should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
B
Option B is correct because the Azure Computer Vision Image Analysis API includes a 'caption' feature that generates a human-readable textual description of an image's content. This prebuilt capability is specifically designed to assist visually impaired users by automatically producing alt-text for images, making it the ideal choice for the social media platform's requirement.
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.
✗
A
Why it's wrong here
Optical Character Recognition (OCR) extracts printed or handwritten text from images but does not generate descriptive sentences.
✓
B
Why this is correct
Image Analysis - Describe Image generates a complete sentence describing the image content, ideal for accessibility purposes.
Related concept
Read the scenario before looking for a memorised answer.
✗
C
Why it's wrong here
Face Detection locates human faces and can provide attributes like age or emotion, but it does not produce a general description.
✗
D
Why it's wrong here
Object Detection identifies objects and their locations with bounding boxes but does not create a narrative description of the scene.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse object detection (which lists objects) with image captioning (which describes the scene), leading them to select object detection when the question explicitly asks for a textual description of the entire image.
Detailed technical explanation
How to think about this question
The Image Analysis API uses a deep neural network trained on millions of images to generate captions via a transformer-based model that maps visual features to natural language. Under the hood, the API returns a 'description' object containing an array of captions with confidence scores, allowing developers to select the most relevant description. In a real-world scenario, the platform could use the 'maxCandidates' parameter to request multiple caption options and choose the highest-confidence one for accessibility compliance.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
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: B — Option B is correct because the Azure Computer Vision Image Analysis API includes a 'caption' feature that generates a human-readable textual description of an image's content. This prebuilt capability is specifically designed to assist visually impaired users by automatically producing alt-text for images, making it the ideal choice for the social media platform's requirement.
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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