Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A retail warehouse uses a camera system to locate and count boxes on shelves. The system needs to output the exact positions of each box by drawing a rectangular frame around it in the image. Which Azure Computer Vision capability should they use?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Best answer
Object detection
Object detection finds objects and returns their bounding boxes, which is precisely what is needed to locate and frame each box in an image.
B
Distractor review
Image classification
Image classifies the entire image into a category (e.g., 'shelf with boxes') but does not locate individual objects within the image.
C
Distractor review
Semantic segmentation
Semantic segmentation assigns a class label to each pixel, creating a pixel‑level mask rather than rectangular bounding boxes.
D
Distractor review
Optical Character Recognition (OCR)
OCR extracts text from images, not the positions of physical objects like boxes.
Common exam trap
Common exam trap: usable hosts are not the same as total addresses
Subnetting questions often tempt you into counting all addresses. In normal IPv4 subnets, the network and broadcast addresses are not usable host addresses.
Technical deep dive
How to think about this question
Subnetting questions test whether you can identify the network, broadcast address, usable range, mask and correct subnet. Slow down enough to calculate the block size correctly.
KKey Concepts to Remember
- CIDR notation defines the prefix length.
- Block size helps identify subnet boundaries.
- Network and broadcast addresses are not usable hosts in normal IPv4 subnets.
- The required host count determines the smallest suitable subnet.
TExam Day Tips
- Write the block size before choosing the subnet.
- Check whether the question asks for hosts, subnets or a specific address range.
- Do not confuse /24, /25, /26 and /27 host counts.
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
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Question 5
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Question 6
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FAQ
Questions learners often ask
What does this AI-900 question test?
CIDR notation defines the prefix length.
What is the correct answer to this question?
The correct answer is: Object detection — Object detection identifies objects within an image and returns their positions as bounding box coordinates. This exactly matches the requirement of drawing a rectangular frame around each box. Image classification only assigns a label to the whole image, not per-object location. Semantic segmentation labels every pixel and produces masks, not bounding boxes. Optical character recognition (OCR) extracts text, not object locations.
What should I do if I get this AI-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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