Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
An autonomous vehicle team needs a system that not only identifies objects like cars and pedestrians but also creates a precise pixel-level mask for each individual object instance, even when objects overlap. 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
Distractor review
Image classification
Image classification assigns a label to the whole image, with no object localization or pixel masks.
B
Distractor review
Object detection
Object detection provides bounding boxes around objects but not pixel-level segmentation masks for individual instances.
C
Distractor review
Semantic segmentation
Semantic segmentation labels each pixel with a class (e.g., car, road) but does not distinguish between different instances of the same class.
D
Best answer
Instance segmentation
Instance segmentation provides a separate segmentation mask for each object instance, enabling precise separation even when objects overlap.
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
A developer is using Azure OpenAI Service to generate product descriptions. They want the output to be highly focused and deterministic, with less randomness. Which parameter should they decrease?
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 extends object detection by providing a pixel-level segmentation mask for each distinct object instance. Semantic segmentation only labels each pixel with a class but does not separate instances. Object detection provides bounding boxes but not masks. Image classification assigns a label to the entire image.
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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