The answer is Object Detection. This is the correct choice because Object Detection goes beyond simply classifying an image as containing a scratch, dent, or crack; it also outputs bounding box coordinates that precisely locate each defect within the product image, directly meeting the requirement to both identify and locate specific defect types on the assembly line. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of the core difference between Image Classification (which answers “what is in the image?”) and Object Detection (which answers “what is it and where is it?”). A common trap is choosing Image Classification because it can label defect types, but it cannot provide location data. Remember the memory tip: “Classification tells you the ‘what,’ Object Detection gives you the ‘where’ and the ‘what’.”
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. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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 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?
Why wrong: Image classification assigns a single label to the whole image (e.g., 'defective' or 'non-defective'), but does not locate multiple defects or distinguish between defect types in different regions.
B
Object Detection
Object detection can both classify and localize multiple objects (defects) in an image, providing bounding boxes around each defect type, which matches the requirement.
C
Optical Character Recognition (OCR)
Why wrong: OCR is used to extract printed or handwritten text from images; it does not recognize physical defects or their locations.
D
Face Detection
Why wrong: Face detection is specialized for detecting human faces in images, not for identifying product defects.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 to the whole image (e.g., 'defective' or 'non-defective'), but does not locate multiple defects or distinguish between defect types in different regions.
✓
Object Detection
Why this is correct
Object detection can both classify and localize multiple objects (defects) in an image, providing bounding boxes around each defect type, which matches the requirement.
Related concept
Read the scenario before looking for a memorised answer.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR is used to extract printed or handwritten text from images; it does not recognize physical defects or their locations.
✗
Face Detection
Why it's wrong here
Face detection is specialized for detecting human faces in images, not for identifying product 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 both classifies and localizes), missing the critical 'locate' requirement in the question.
Detailed technical explanation
How to think about this question
Azure Custom Vision's Object Detection model uses a convolutional neural network (CNN) with a region proposal network (RPN) to output bounding boxes and class probabilities for each detected object. In a real-world assembly line, this allows the system to flag a scratch at pixel coordinates (x1,y1,x2,y2) and a dent at a different location, enabling precise robotic rejection or rework.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
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
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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