AI-900 Practice Question: Describe features of computer vision workloads on Azure
A security company needs to analyze live video feeds from multiple cameras to detect specific objects (e.g., vehicles, people) and also read license plate numbers from vehicles. Which combination of Azure Computer Vision capabilities should they use?
⚠ Common exam trap
A common mix-up: candidates confuse Image Analysis (which provides tags and descriptions) with Object Detection, or assume Face Detection can be generalized to other objects, leading them to choose Option B instead of the correct combination of Object Detection and OCR.
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
✓
Object detection and Optical Character Recognition
The scenario requires two distinct capabilities: detecting specific objects (vehicles, people) in live video feeds, which is handled by Azure Computer Vision's Object Detection feature, and reading license plate numbers, which requires Optical Character Recognition (OCR). Object detection identifies and locates objects within an image or video frame, while OCR extracts text from images, making this combination ideal for the use case.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Object detection and Optical Character Recognition
Why this is correct
Object detection uses bounding boxes to identify and locate moving or stationary vehicles and people in each video frame, outputting their coordinates and a class label. Optical Character Recognition (OCR) reads the alphanumeric characters printed on any visible license plate, returning the plate text as machine-readable output. Together, these two Azure Computer Vision services meet the dual need to detect relevant entities and extract plate identifiers from live feeds.
- ✗
Image analysis and face detection
Why it's wrong here
Image analysis in Azure Computer Vision returns tags, a caption, and a dense set of detected objects, but it does not output a focused, bounding-box-localized set of vehicle and person detections with coordinates in the way object detection does. Face detection locates human faces and gives face attributes, yet it ignores vehicles entirely and cannot extract text from license plates. Consequently, this combination cannot meet the requirement to detect vehicles and read plate characters.
- ✗
Semantic segmentation and image captioning
Why it's wrong here
Semantic segmentation assigns a class to every pixel in the frame, producing a pixel-level mask of surfaces such as road, sky, or car, but it performs no text-reading operation. Image captioning generates a natural-language sentence describing the overall scene, for example 'a car driving in a parking lot,' rather than returning the actual alphanumeric characters of a license plate. The output is therefore too coarse for the mandated vehicle detection and plate-text extraction.
- ✗
Spatial analysis and image classification
Why it's wrong here
Spatial analysis tracks the presence, movement, and distance of people in a video frame using person-detection and tracking logic, but it is not designed to enumerate vehicles or read text. Image classification labels the entire image with a single category such as 'traffic scene,' which gives no object localization or plate content. This pairing lacks the required capability to localize vehicles and extract license plate text with Optical Character Recognition.
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
Key term
OCR
Optical Character Recognition is technology that converts images of text, like scanned documents or photos, into machine-readable text data.
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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