AI-900 Practice Question: Describe features of computer vision workloads on Azure
A retail chain wants to automatically detect which specific products are missing from store shelves by analyzing images from in-store cameras. Each product has a distinct shape and label. Which Azure Computer Vision capability is most appropriate for this task?
⚠ Common exam trap
Many candidates confuse Image Classification with Object Detection, thinking that classifying the entire image as 'shelf with products' is sufficient, but the task requires locating and identifying individual missing products, which only Object Detection can do.
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
✓
B) Object Detection
Object Detection (Option B) is the correct choice because it can identify and locate multiple products within an image by drawing bounding boxes around each detected object. This allows the system to determine which specific products are missing by comparing detected items against an expected inventory list. Image Classification would only label the entire image, not individual products, while OCR focuses on text extraction and Facial Recognition identifies people.
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) Image Classification
Why it's wrong here
Image classification assigns a single categorical label to the entire input image using a model such as a convolutional neural network with a softmax output. It produces no bounding boxes or instance counts, so in a retail shelf scene it cannot distinguish one product from another or identify which specific SKUs are missing. At most, it could label the whole scene as e.g. 'stocked shelf' or 'empty shelf'.
- ✓
B) Object Detection
Why this is correct
Object detection both localizes and classifies each object in an image by outputting bounding boxes and class labels for every detected instance. Architectures like Faster R-CNN or YOLO generate region proposals or grid-based predictions, allowing the system to count and identify each product on a shelf. By comparing the detected products against an expected planogram, the system can pinpoint exactly which specific items are absent.
- ✗
C) Optical Character Recognition (OCR)
Why it's wrong here
OCR converts images of text into machine-readable character strings, using techniques like text detection, character segmentation, and recognition. It can read a product label or barcode only if visible text is present and legible, but it provides no geometric or semantic understanding of the physical product itself. If a can or bottle is present yet its label is rotated, obscured, or absent, OCR cannot determine whether that product is on the shelf.
- ✗
D) Facial Recognition
Why it's wrong here
Facial recognition is a specialized biometric task that detects human faces, extracts face embeddings, and matches them against a known gallery of identities. Its models are trained on facial features and are not designed to represent arbitrary product packaging or distinguish different retail SKUs. Applying facial recognition to a shelf image would fail because it lacks the category space and localization structure needed to identify products, let alone missing items.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
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