AI-900 Practice Question: Describe features of computer vision workloads on Azure
What does Azure AI Vision's image tagging feature return?
⚠ Common exam trap
Many 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.
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
✓
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.
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 palette extraction is a separate Azure AI Vision capability that returns dominant colors as hex codes in a structured JSON output. Image tagging, in contrast, produces semantic keyword labels—such as 'person', 'dog', 'beach'—with confidence scores, rather than a color-specific data structure. The request itself determines the operation, and the tagging endpoint does not return color palettes.
- ✓
A list of descriptive keywords about the image content with confidence scores
Why this is correct
Image tagging uses a trained deep-learning model to analyze pixel content and generate a list of descriptive keywords (tags) that identify objects, actions, scenes, and even colors, each paired with a confidence score between 0 and 1. The tags are returned in descending order of confidence, allowing downstream applications to rank the most likely interpretations. This output directly matches the question's description of the feature.
- ✗
GPS coordinates of where the photo was taken
Why it's wrong here
GPS coordinates are another form of EXIF metadata that gets embedded when a device has geotagging enabled. The image tagging feature works exclusively on the visual content of the image—analyzing shapes, colors, and patterns—and does not access any embedded geographic information. Returning location data would require a separate metadata extraction call or a specialized geo-tagging service.
- ✗
The camera settings used to capture the image
Why it's wrong here
Camera settings such as exposure, aperture, and ISO are stored in the image's EXIF metadata, which is written by the camera at capture time and is not part of the visual content itself. Image tagging analyzes only the pixel values of the image through computer vision, so it never reads or returns EXIF data. To retrieve camera settings, a separate metadata-parsing operation would be required.
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Azure Machine Learning Studio
Key term
Confidence score
A confidence score is a number (often between 0 and 1 or 0 and 100%) that tells you how likely it is that an AI model's prediction or answer is correct.
Key term
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
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