The answer is Custom Vision object detection. This Azure Computer Vision capability is the correct choice because it goes beyond simple image classification by not only determining whether a product is defective or non-defective but also localizing the exact position of the defect—such as a crack—by drawing bounding boxes around it. The labeled dataset with defect locations directly supports training a model to output both class labels and spatial coordinates, which is precisely what object detection provides. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of the difference between image classification (which only labels the whole image) and object detection (which adds localization). A common trap is choosing Custom Vision image classification, which cannot pinpoint where a defect is. Remember the memory tip: “Detection draws boxes; classification only labels the whole.”
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: custom Vision object detection identifies and locates multiple objects within 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 wants to use Azure Computer Vision to inspect products on an assembly line for defects. They have a labeled dataset with images of defective and non-defective products. They need to not only classify products as defective or not, but also identify the exact location of the defect (e.g., a crack) in the image. Which Azure Computer Vision capability should they use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Custom Vision object detection
Custom Vision object detection is the correct choice because it not only classifies images (defective vs. non-defective) but also localizes defects by drawing bounding boxes around them. The labeled dataset with defect locations directly supports training a model to output both class labels and spatial coordinates, which is exactly what object detection provides.
Key principle: Custom Vision object detection identifies and locates multiple objects within 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.
✓
Custom Vision object detection
Why this is correct
Correct. Object detection can be trained to identify and locate defects (e.g., cracks) within an image, providing both classification and location.
Related concept
Custom Vision object detection identifies and locates multiple objects within an image.
✗
Custom Vision image classification
Why it's wrong here
Image classification only labels the entire image (e.g., defective or not) and does not indicate where the defect is located.
✗
Azure Face API
Why it's wrong here
Face API is designed for detecting and analyzing human faces, not for general defect detection.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR is used to extract printed or handwritten text from images, not for detecting defects.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse image classification (which only labels the whole image) with object detection (which provides both classification and localization), leading them to choose Custom Vision image classification despite the explicit need for defect location.
Detailed technical explanation
How to think about this question
Custom Vision object detection uses a deep neural network (e.g., Faster R-CNN or YOLO variant) that outputs bounding box coordinates (x, y, width, height) and class probabilities for each detected object. During training, the model learns to minimize both classification loss and localization loss (e.g., smooth L1 loss for bounding box regression). In a real-world assembly line scenario, the model can be deployed to Azure IoT Edge for real-time inference at the edge, reducing latency and bandwidth usage.
KKey Concepts to Remember
Custom Vision object detection identifies and locates multiple objects within an image.
It outputs bounding box coordinates and class labels for each detected object.
Object detection requires bounding box annotations during the training phase.
It is ideal for scenarios needing precise localization of features or defects.
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
Custom Vision object detection identifies and locates multiple objects within 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.
Review custom Vision object detection identifies and locates multiple objects within an image., then practise related AI-900 questions on the same topic to reinforce the concept.
Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — Custom Vision object detection identifies and locates multiple objects within an image..
What is the correct answer to this question?
The correct answer is: Custom Vision object detection — Custom Vision object detection is the correct choice because it not only classifies images (defective vs. non-defective) but also localizes defects by drawing bounding boxes around them. The labeled dataset with defect locations directly supports training a model to output both class labels and spatial coordinates, which is exactly what object detection provides.
What should I do if I get this AI-900 question wrong?
Review custom Vision object detection identifies and locates multiple objects within an image., then practise related AI-900 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Custom Vision object detection identifies and locates multiple objects within an image.
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