AI-900 Practice Question: Describe features of computer vision workloads on Azure
A logistics company uses security cameras to monitor boxes on warehouse shelves. They need an AI solution that can count the number of boxes on each shelf and also identify if any box is red (indicating a priority shipment). Which Azure Computer Vision capability should they use?
⚠ Common exam trap
A common mix-up: candidates confuse object detection with OCR or spatial analysis, thinking text extraction or motion tracking could somehow count boxes or detect colors, when in fact object detection is the only option that can both localize objects and support color analysis.
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
✓
Image Analysis (object detection)
Image Analysis with object detection can identify and localize multiple objects (boxes) within an image, count them, and detect specific attributes like color (red boxes) by analyzing pixel values in the detected bounding boxes. This directly meets the requirement to count boxes and identify priority shipments based on color.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Image Analysis (object detection)
Why this is correct
Azure AI Vision's Image Analysis object detection scans the image for instances of common objects and returns a bounding box, class label, and confidence score for each detected instance. Because it detects multiple objects in a single call, it can count how many boxes appear in the camera view and extract visual properties such as color, which directly supports the logistics monitoring scenario. Unlike image classification, object detection localizes each object instead of merely labeling the whole image.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR is a specialized computer vision capability for extracting printed or handwritten text from images, returning recognized strings and their locations. It does not generate object labels or bounding boxes for physical items like boxes, and it cannot determine their color or count them by visual appearance. In this scenario, OCR might read a label on a box, but it cannot substitute for object detection when the goal is counting boxes or extracting their visual attributes.
- ✗
Face detection
Why it's wrong here
Face detection is a focused computer vision API designed to locate human faces, returning face rectangles and landmarks, and optionally face attributes such as age or emotion. It does not process generic inanimate objects because a cardboard box has no facial landmarks, so the service would return no detections and could not count boxes or determine their color. While useful for security when identifying people, it is not relevant to monitoring inanimate shipments.
- ✗
Spatial analysis
Why it's wrong here
Spatial analysis processes video streams to track moving people and objects over time, including measures such as person presence, dwell time, and zone-crossing events, rather than analyzing a single still image to inventory static objects. It does not perform static object counting and does not return class labels or color metadata for boxes. Spatial analysis could complement the scenario by tracking workers near the boxes, but it would not answer how many boxes are present in a given camera frame.
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Azure Machine Learning Studio
Key term
Computer vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
Key term
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
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