AI-900 Practice Question: Describe features of computer vision workloads on Azure
A manufacturing company wants to use computer vision to inspect products on an assembly line. They need to identify and locate specific types of defects (e.g., scratch, dent, crack) in product images. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Test-takers frequently confuse Image Classification (which only labels the whole image) with Object Detection (which both classifies and localizes), missing the critical 'locate' requirement in the question.
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 choice because it not only classifies defects (e.g., scratch, dent, crack) but also provides bounding box coordinates to locate each defect within the product image. This meets the requirement to both identify and locate specific defect types on the assembly line.
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 an entire image by aggregating features globally, which cannot distinguish multiple defects at different positions. For manufacturing inspection, this would merely output 'defective' or 'non-defective' without bounding boxes or per-defect type counts, so it lacks the spatial information required. Serving only as a coarse pass/fail gate, image classification fails when the requirement is to locate every defect and identify each type in one image.
- ✓
Object Detection
Why this is correct
Object detection performs both classification and localization by outputting bounding boxes around each detected instance, along with a class label and confidence score. Models such as Faster R-CNN, YOLO, and SSD use region proposals or anchor-based regression to predict box coordinates for every object. In this inspection scenario, that allows the system to report each defect's position and type, exactly matching the requirement to identify and locate multiple defects.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
Optical Character Recognition (OCR) detects and extracts textual characters from images, using a pipeline that isolates text regions and recognizes glyphs through pattern matching. Because OCR models are trained specifically on letters and numbers, they are blind to non-text surface flaws such as scratches, dents, or stains. Applying OCR to inspect manufactured parts would therefore return no meaningful bounding boxes for fabrication defects, making it irrelevant to quality control.
- ✗
Face Detection
Why it's wrong here
Face detection is a specialized object-detection variant that recognizes facial landmarks and skin-tone features, so its learned representations are tightly coupled to human anatomy. Product defects like cracks or weld imperfections do not share these anthropometric patterns, and a face detector would either ignore them or fail to generalize. By contrast, the requirement calls for generic defect recognition, which needs domain-specific training on industrial imagery, not a face-specific model.
Go deeper
Related to this question
Learn chapter
Types of AI Workloads
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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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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