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 cosmetic defects such as scratches, dents, or color inconsistencies. They need to classify each product as 'defective' or 'non-defective' and also identify the precise region (e.g., a specific area of the product surface) that contains the defect. Which Azure Computer Vision capability should they use?
⚠ Common exam trap
Watch out — candidates often confuse object detection (bounding boxes) with semantic segmentation (pixel-level masks), assuming bounding boxes are sufficient for precise defect localization, but the question explicitly requires identifying the 'precise region' of the defect, which demands pixel-level accuracy.
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
✓
Semantic segmentation
Semantic segmentation is the correct choice because it assigns a class label (e.g., 'defective' or 'non-defective') to every pixel in the image, enabling the model to not only classify the product but also delineate the exact boundary of the defect region. This pixel-level precision is required to identify the precise area of the product surface containing the scratch, dent, or color inconsistency.
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 label (for example, 'defective' or 'non-defective') to an entire image based on its dominant content. It can indicate whether some defect exists somewhere in the frame, but it cannot answer where the defect is or how large it is. In quality control, that lack of spatial localization makes it inadequate for guiding automated rejection, repair, or root-cause analysis when a defect may occupy only a tiny region of the camera view.
- ✗
Object detection
Why it's wrong here
Object detection finds discrete objects (such as a person, car, or product) and returns their bounding boxes or class labels. Defects like hairline cracks, stains, or porosity are usually irregular, elongated, or fragmented and do not form well-defined rectangular objects, so a detector would either miss them or enclose large healthy areas in its box. Even when a bounding box is placed, it cannot provide the sub-pixel or ragged-boundary detail needed to measure defect area or shape accurately.
- ✓
Semantic segmentation
Why this is correct
Semantic segmentation performs dense per-pixel classification, meaning every pixel in the camera image is labeled as either part of a defect or part of a healthy surface. It produces a precise, pixel-aligned mask that naturally follows irregular contours, allowing exact measurement of defect size, shape, and location. This makes it the best fit for assembly-line visual inspection where defects vary widely in geometry and must be detected with pixel-level accuracy.
- ✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR is a technique for extracting machine-encoded or handwritten text from scanned documents and images by recognizing characters and words. It is entirely text-focused and has no capability to interpret visual surface anomalies such as scratches, cracks, or discolorations. Since assembly-line defects are non-textual patterns, applying OCR would simply return no useful character data and would ignore the actual defect signal.
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
Key term
Label
A label is a piece of metadata attached to data, objects, or resources to identify, classify, or describe them for easier management and retrieval.
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.
About these practice questions
Courseiva writes every AI-900 question from scratch — 985 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.