The correct answer is a list of descriptive keywords about the image content with confidence scores. Azure AI Vision’s image tagging feature works by analyzing the visual features of an image—such as objects, actions, and scenery—and then generating a set of relevant tags, each paired with a confidence score that indicates how likely the tag accurately describes the image. This is distinct from OCR or facial recognition, as tagging focuses on broad content identification rather than specific text or faces. On the AI-900 exam, this concept tests your understanding of Azure’s pre-built vision capabilities, and a common trap is confusing tagging with image classification (which returns a single category) or object detection (which returns bounding boxes). Remember that tagging returns multiple tags with scores, not just one label. A helpful memory tip: think of “tagging” like hashtags on social media—each image gets a list of descriptive words, each with a probability rating.
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 Azure AI Vision's image tagging feature return?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A list of descriptive keywords about the image content with confidence scores
Azure AI Vision's image tagging feature analyzes the content of an image and returns a list of descriptive keywords (tags) along with a confidence score for each tag. This allows applications to automatically identify objects, people, scenes, and actions within the image without requiring manual labeling.
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 JSON file with the image's color palette in hex codes
Why it's wrong here
Color extraction is a separate feature — image tagging returns descriptive content keywords.
✓
A list of descriptive keywords about the image content with confidence scores
Why this is correct
Image tagging returns keyword tags describing objects, scenes, activities, and colors in the image with confidence scores.
Related concept
Read the scenario before looking for a memorised answer.
✗
GPS coordinates of where the photo was taken
Why it's wrong here
Location data comes from image EXIF metadata — image tagging analyzes visual content.
✗
The camera settings used to capture the image
Why it's wrong here
Camera settings are in EXIF data — image tagging analyzes visual content to generate semantic keywords.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse image tagging with other image analysis features like optical character recognition (OCR), face detection, or metadata extraction, leading them to select options that describe unrelated capabilities.
Trap categories for this question
Keyword trap
Color extraction is a separate feature — image tagging returns descriptive content keywords.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Vision uses deep neural networks trained on millions of labeled images to generate tags. The confidence score (ranging from 0 to 1) indicates the model's certainty that the tag accurately describes the image content. In real-world scenarios, tagging is used for automated cataloging, content moderation, and accessibility features like generating alt text 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: A list of descriptive keywords about the image content with confidence scores — Azure AI Vision's image tagging feature analyzes the content of an image and returns a list of descriptive keywords (tags) along with a confidence score for each tag. This allows applications to automatically identify objects, people, scenes, and actions within the image without requiring manual labeling.
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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