Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
A logistics company uses drone imagery to monitor a busy container yard. They need to count the exact number of individual shipping containers, even when containers are partially stacked on top of each other or overlapping in the image. Which Azure Computer Vision capability should they choose to achieve the most accurate individual object separation?
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
Distractor review
Image classification
Image classification assigns a single label to the entire image (e.g., 'container yard'), not individual objects or their instances.
B
Distractor review
Object detection
Object detection draws bounding boxes around objects. For overlapping containers, bounding boxes may merge or fail to separate individual containers, reducing counting accuracy.
C
Best answer
Instance segmentation
Instance segmentation identifies each object instance separately and produces a pixel-level mask for each, enabling accurate counting even when objects overlap.
D
Distractor review
Semantic segmentation
Semantic segmentation classifies every pixel into a category (e.g., 'container') but does not differentiate between individual containers, so it cannot provide an instance count.
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 2
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Question 3
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Question 4
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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: Instance segmentation — Instance segmentation provides a pixel-level mask for each detected object, allowing precise separation even when objects overlap. Object detection (Option B) uses bounding boxes that can overlap and merge for stacked containers, reducing accuracy. Semantic segmentation (Option C) classifies every pixel by class (e.g., 'container') but does not distinguish between individual container instances. Optical character recognition (Option D) extracts text, which is irrelevant for counting containers.
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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