- A
Only the file size and dimensions of the image
Why wrong: File metadata is basic image information — Image Analysis returns rich semantic information about image content.
- B
Descriptions, objects, tags, and other semantic information about the image content
Image Analysis returns natural language descriptions, detected objects, tags, categories, and other semantic information about what's in the image.
- C
Only a single category label for the entire image
Why wrong: Image Analysis returns multiple types of information — single-label classification is what Custom Vision can do with a custom model.
- D
A 3D point cloud of the scene
Why wrong: 3D reconstruction requires specialized depth sensors — Image Analysis works with standard 2D images for semantic understanding.
Quick Answer
The correct answer is that Azure AI Vision Image Analysis returns descriptions, objects, tags, and other semantic information about the image content. This is because the service leverages pre-trained deep learning models to go beyond simple metadata, extracting rich, human-readable insights such as a natural language caption describing the scene, a list of detected objects with their bounding box coordinates, and a set of relevant tags that categorize the image’s themes. On the AI-900 exam, this question tests your understanding of the breadth of Azure AI Vision’s capabilities, often appearing as a multiple-choice trap where options like “only metadata” or “only tags” are incorrect because they omit the full semantic output. A common memory tip is to think of the acronym DOT: Descriptions, Objects, and Tags—the three core semantic returns that distinguish Image Analysis from basic image processing.
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.
What does the Azure AI Vision 'Image Analysis' capability return when analyzing an image?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Descriptions, objects, tags, and other semantic information about the image content
Azure AI Vision's Image Analysis capability uses pre-trained deep learning models to extract rich semantic information from images, including human-readable descriptions, a list of detected objects with bounding boxes, and a set of relevant tags. This goes far beyond basic metadata, making option B correct because it accurately captures the breadth of semantic outputs the service provides.
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.
- ✗
Only the file size and dimensions of the image
Why it's wrong here
File metadata is basic image information — Image Analysis returns rich semantic information about image content.
- ✓
Descriptions, objects, tags, and other semantic information about the image content
Why this is correct
Image Analysis returns natural language descriptions, detected objects, tags, categories, and other semantic information about what's in the image.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Only a single category label for the entire image
Why it's wrong here
Image Analysis returns multiple types of information — single-label classification is what Custom Vision can do with a custom model.
- ✗
A 3D point cloud of the scene
Why it's wrong here
3D reconstruction requires specialized depth sensors — Image Analysis works with standard 2D images for semantic understanding.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse basic image metadata (file size, dimensions) with the semantic analysis outputs of Azure AI Vision, leading them to choose option A, or they assume the service only returns a single label (option C) because they think of simpler classification models rather than the multi-output analysis capability.
Detailed technical explanation
How to think about this question
Under the hood, Image Analysis leverages convolutional neural networks (CNNs) trained on large datasets like COCO and ImageNet to perform object detection, scene classification, and caption generation. The API returns a JSON response with fields such as 'description.captions' (with confidence scores), 'tags' (with confidence scores), and 'objects' (with bounding box coordinates). A real-world scenario is an e-commerce platform that uses Image Analysis to automatically generate alt-text for product images and tag items for search indexing, improving accessibility and discoverability.
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.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
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: Descriptions, objects, tags, and other semantic information about the image content — Azure AI Vision's Image Analysis capability uses pre-trained deep learning models to extract rich semantic information from images, including human-readable descriptions, a list of detected objects with bounding boxes, and a set of relevant tags. This goes far beyond basic metadata, making option B correct because it accurately captures the breadth of semantic outputs the service provides.
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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Last reviewed: Jun 11, 2026
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