AI-900 Practice Question: Describe features of computer vision workloads on Azure
This AI-900 practice question tests your understanding of describe features of computer vision workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
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?
Why wrong: OCR extracts printed or handwritten text from images; it does not detect general objects or count items.
B
Image classification
Why wrong: 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
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
Semantic segmentation
Why wrong: 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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Object detection
Object detection (Option C) is the correct prebuilt Computer Vision capability because it can both locate items within an image using bounding boxes and count them, without requiring identification of the specific product type. This aligns directly with the requirement to detect presence and count items on a shelf, as object detection outputs the coordinates and count of detected objects, not their classification into fine-grained categories.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts printed or handwritten text from images; it does not detect general objects or count items.
✗
Image classification
Why it's wrong here
Image classification assigns a category (e.g., 'shelf with items') to the whole image, but it cannot count multiple items or indicate their positions.
✓
Object detection
Why this is correct
Object detection identifies each object instance, provides bounding boxes, and allows counting of detected objects, even if product types are not distinguished.
Related concept
Read the scenario before looking for a memorised answer.
✗
Semantic segmentation
Why it's wrong here
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 traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse object detection with image classification, assuming that classifying the shelf as 'stocked' or 'empty' is sufficient, but the question explicitly requires counting individual items, which only object detection can provide.
Detailed technical explanation
How to think about this question
Under the hood, Azure Computer Vision's object detection uses a deep neural network (e.g., YOLO or Faster R-CNN) that outputs bounding box coordinates, confidence scores, and class labels for each detected object. In this scenario, the model can be configured to detect a generic 'item' class, allowing counting without product-specific identification. A subtle behavior is that object detection can still count items even if they are partially occluded, as long as the confidence threshold is tuned appropriately.
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.
TExam Day Tips
→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.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of computer vision workloads on Azure — This question tests Describe features of computer vision workloads on Azure — 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 (Option C) is the correct prebuilt Computer Vision capability because it can both locate items within an image using bounding boxes and count them, without requiring identification of the specific product type. This aligns directly with the requirement to detect presence and count items on a shelf, as object detection outputs the coordinates and count of detected objects, not their classification into fine-grained categories.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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