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AI-900 Practice Question: 'object detection' in computer vision and how…
This AI-900 practice question tests your understanding of 'object detection' in computer vision and how…. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
What is 'object detection' in computer vision and how does it differ from image classification?
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
Object detection only works on images with a single object; classification handles multiple objects
This is backwards — object detection is specifically designed to find multiple objects; classification produces one label for the whole image.
B
Distractor review
Image classification processes images faster than object detection because it is simpler
Speed differences exist but the key distinction is functional — classification labels the image, detection locates and labels individual objects.
C
Best answer
Object detection locates each object with a bounding box and class label; classification labels the whole image
Detection = where are the objects AND what are they? Classification = what is the dominant content of this image?
D
Distractor review
Object detection and image classification produce the same output — both label the entire image
Classification labels the whole image; detection adds per-object location — they produce very different outputs.
Common exam trap
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Technical deep dive
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
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FAQ
Questions learners often ask
What does this AI-900 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Object detection locates each object with a bounding box and class label; classification labels the whole image — Image classification labels the entire image with a single class ('cat'). Object detection identifies multiple objects within an image, returning a bounding box location AND a class label for each object — so the output might be 'cat at (100,200,300,400), dog at (50,150,200,350)'. Object detection is harder and more useful when an image may contain multiple objects of interest at different locations.
What should I do if I get this AI-900 question wrong?
Identify which AI-900 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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