The answer is Image Tagging, as this Azure Computer Vision capability is specifically designed to analyze visual content and return a set of relevant keywords based on detected objects, scenes, and concepts. When a digital art library needs to automatically generate keywords like 'landscape', 'portrait', or 'abstract' for each image, Image Tagging directly fulfills that requirement by extracting descriptive tags from the visual elements present. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of how Computer Vision services map to real-world tasks, often appearing as a scenario where you must distinguish Image Tagging from similar features like Optical Character Recognition (OCR) or Image Captioning. A common trap is confusing Image Tagging with object detection—remember that tagging returns descriptive keywords, not bounding boxes or coordinates. For a quick memory tip, think of Image Tagging as the "what’s in the picture" feature: it reads the scene and spits out a list of words, making it the perfect tool for automated keyword generation.
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 digital art library wants to automatically generate a list of relevant keywords (e.g., 'landscape', 'portrait', 'abstract', 'nature') for each image in their collection. Which Azure Computer Vision capability 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
✓
Image Tagging
Image Tagging (B) is the correct capability because it analyzes the content of an image and returns a set of relevant keywords (tags) based on the detected objects, scenes, and concepts. This directly matches the requirement to generate a list of keywords like 'landscape', 'portrait', 'abstract', and 'nature' for each image.
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, not descriptive keywords.
✓
Image Tagging
Why this is correct
Image Tagging automatically generates a list of relevant keywords or tags that describe the content of the image.
Related concept
Read the scenario before looking for a memorised answer.
✗
Image Captioning
Why it's wrong here
Image Captioning generates a full sentence description of the image, not a list of keywords.
✗
Object Detection
Why it's wrong here
Object Detection identifies and locates objects with bounding boxes but does not produce a list of general keywords.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Image Captioning (which produces a single sentence) with Image Tagging (which produces a list of keywords), or they assume Object Detection is needed because it identifies objects, but it does not return a simple keyword list.
Trap categories for this question
Keyword trap
OCR extracts printed or handwritten text from images, not descriptive keywords.
Detailed technical explanation
How to think about this question
Azure Computer Vision's Image Tagging uses a deep learning model trained on millions of images to assign tags from a predefined taxonomy of thousands of concepts. The service returns each tag with a confidence score, allowing the library to filter or rank keywords. Under the hood, the model uses a convolutional neural network (CNN) to extract visual features and map them to semantic labels, which can include both objects (e.g., 'tree') and abstract concepts (e.g., 'nature').
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 Tagging — Image Tagging (B) is the correct capability because it analyzes the content of an image and returns a set of relevant keywords (tags) based on the detected objects, scenes, and concepts. This directly matches the requirement to generate a list of keywords like 'landscape', 'portrait', 'abstract', and 'nature' for each image.
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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