The answer is semantic segmentation. This Azure Computer Vision capability is the correct choice because it classifies every pixel in an image, assigning a label such as 'defective' or 'non-defective' to each one, which allows the model to delineate the exact boundary of a scratch, dent, or color inconsistency at the pixel level. For the AI-900 exam, this question tests your understanding of how semantic segmentation differs from image classification (which only labels the whole image) and object detection (which uses bounding boxes). A common trap is confusing semantic segmentation with object detection, but remember: only semantic segmentation provides the precise, pixel-level defect region required for manufacturing quality control. Memory tip: think "Semantic = Segment = Seamless pixel maps," so when you need to pinpoint a defect's exact shape and location, semantic segmentation is your go-to tool.
AI-900 Practice Question: Describe features of computer vision workloads on Azure
This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. A key principle to apply: semantic segmentation classifies every pixel in an image.. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Why wrong: Incorrect. Image classification returns a single label for the whole image and cannot pinpoint the location of a defect.
B
Object detection
Why wrong: Incorrect. Object detection identifies objects and their bounding boxes, but defects are often irregular shapes and not considered separate objects; it would not provide pixel-level detail.
C
Semantic segmentation
Correct. Semantic segmentation classifies every pixel, enabling the model to identify defective regions with high precision, even for irregular shapes.
D
Optical Character Recognition (OCR)
Why wrong: Incorrect. OCR extracts text from images and is not designed to detect visual defects.
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.
Key principle: Semantic segmentation classifies every pixel in an image.
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
Incorrect. Image classification returns a single label for the whole image and cannot pinpoint the location of a defect.
✗
Object detection
Why it's wrong here
Incorrect. Object detection identifies objects and their bounding boxes, but defects are often irregular shapes and not considered separate objects; it would not provide pixel-level detail.
✓
Semantic segmentation
Why this is correct
Correct. Semantic segmentation classifies every pixel, enabling the model to identify defective regions with high precision, even for irregular shapes.
Related concept
Semantic segmentation classifies every pixel in an image.
✗
Optical Character Recognition (OCR)
Why it's wrong here
Incorrect. OCR extracts text from images and is not designed to detect visual defects.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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.
Detailed technical explanation
How to think about this question
Semantic segmentation uses fully convolutional networks (FCNs) or U-Net architectures to produce a dense prediction map where each pixel is classified. In Azure Custom Vision, semantic segmentation is supported via the 'Segmentation' project type, which requires polygon annotations for training. A real-world scenario is detecting hairline cracks on a ceramic tile: bounding boxes would include too much background, but semantic segmentation isolates the exact crack pixels for accurate quality control.
KKey Concepts to Remember
Semantic segmentation classifies every pixel in an image.
It creates a precise mask over objects or regions of interest.
Ideal for identifying irregular shapes and boundaries, like defects.
Provides pixel-level detail for accurate localization.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Semantic segmentation classifies every pixel in an image.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Semantic segmentation classifies every pixel in an image..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-900 question wrong?
Review semantic segmentation classifies every pixel in an image., then practise related AI-900 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Semantic segmentation classifies every pixel in an image.
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