Question 1easymultiple choice
Full question →easymultiple choiceObjective-mapped
A retail company wants to use Azure Computer Vision to automatically monitor shelf inventory. They need to detect whether items are present on a shelf and count the number of items, without needing to identify the specific product type. Which prebuilt 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
Optical Character Recognition (OCR)
OCR extracts printed or handwritten text from images; it does not detect general objects or count items.
B
Distractor review
Image classification
Image classification assigns a category (e.g., 'shelf with items') to the whole image, but it cannot count multiple items or indicate their positions.
C
Best answer
Object detection
Object detection identifies each object instance, provides bounding boxes, and allows counting of detected objects, even if product types are not distinguished.
D
Distractor review
Semantic segmentation
Semantic segmentation assigns each pixel to a class (e.g., 'item', 'shelf'), but it is not primarily designed for counting and is less commonly used for simple item counting without custom training.
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.
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?
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 identifies and locates objects within an image, returning bounding boxes and labels. This allows counting of detected objects even if the exact product type is not needed. OCR (A) extracts text, not objects. Image classification (B) assigns a single label to the entire image and does not provide location or count. Semantic segmentation (D) performs pixel-level classification but is more granular than required and not the standard prebuilt offering for simple counting.
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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