AI-900 Practice Question: Describe features of computer vision workloads on Azure
A manufacturing company uses cameras on an assembly line to inspect products for defects such as scratches, dents, and discoloration. They need to identify the specific type of defect and its location on each product. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
It's easy for candidates to confuse object detection with image classification, assuming that identifying the defect type alone is sufficient, but the question explicitly requires both the type and location, which only object detection provides.
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
Object detection is the correct capability because it not only identifies the presence of defects (like scratches, dents, or discoloration) but also localizes each defect by drawing bounding boxes around them. This meets the requirement to both classify the specific defect type and report its location on the product.
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 classification
Why it's wrong here
Image classification assigns a single class label to the entire input image using a convolutional neural network. On an assembly line it could answer only 'defective' or 'not defective' (or one defect type per image), but it cannot output where each defect is located or count multiple defects in the same frame. Because a defect is a localized pattern, a single global label loses spatial information and cannot support decisions like which part to reject or repair.
- ✓
Object detection
Why this is correct
Object detection outputs a set of bounding boxes, each with a class label and a confidence score, so every defect in the camera image is individually identified and localized. This directly supports manufacturing quality control by allowing multiple different defects to be counted, measured, and mapped to physical locations on the assembly line. The approach is a supervised machine learning task, typically using a convolutional neural network architecture such as Faster R-CNN or YOLO, and it matches the requirement to identify and locate defects.
- ✗
Semantic segmentation
Why it's wrong here
Semantic segmentation classifies every pixel into a predefined category, producing a dense pixel-level mask of each defect region rather than a simple bounding box. It is more precise than object detection for characterizing exact shape and area, but it requires far more detailed pixel-level annotation and is computationally heavier. For the stated business need of detecting defect types and bounding their locations, this granularity is more than necessary and would drive up labeling cost without improving the core inspection decision.
- ✗
Optical character recognition
Why it's wrong here
Optical character recognition (OCR) extracts machine-printed or handwritten text from images and converts it into machine-readable strings. It works by detecting character shapes, strokes, and text layouts, so it has no mechanism for recognizing a scratch, crack, or other physical defect on a manufactured part. Applying OCR to an assembly-line image would at best return meaningless text-like patterns and cannot satisfy a defect-detection requirement.
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Azure Machine Learning Studio
Key term
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
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.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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