The answer is object detection. This prebuilt Azure Computer Vision capability is the correct choice because it identifies and locates multiple objects within an image by drawing bounding boxes around each detected item, providing both the presence and spatial position of products like boxes or bottles on a shelf. For the retail scenario, the company needs to know where products are and that they exist, but does not require specific brand or type identification, which aligns perfectly with object detection’s output of class labels (e.g., ‘product’) and coordinates. On the AI-900 exam, this question tests your ability to distinguish between object detection and other capabilities like image classification (which only labels the entire image) or optical character recognition (which reads text). A common trap is confusing object detection with image classification, but remember: detection gives you bounding boxes and locations, while classification only tells you what the whole scene is. Memory tip: think “detection = boxes and positions” for shelf monitoring.
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 monitor product availability on shelves. They need to detect the presence and location of any product (e.g., a box, a bottle) on a shelf image, but they do not need to identify the specific product brand or type. Which prebuilt Azure Computer Vision capability should they use?
Object detection identifies objects and their locations with bounding boxes, which directly fulfills the requirement without needing to identify the specific product type.
B
Image classification
Why wrong: Image classification assigns a label to the entire image but does not provide the location of individual objects.
C
Optical Character Recognition (OCR)
Why wrong: OCR extracts printed or handwritten text from images, not general objects.
D
Semantic segmentation
Why wrong: Semantic segmentation classifies each pixel into a predefined class (e.g., product, shelf) and typically requires a custom trained model, not a prebuilt capability.
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 is the correct choice because it identifies and locates multiple objects within an image by drawing bounding boxes around each detected item. For monitoring product availability on shelves, the company needs to know both the presence and position of products (e.g., boxes, bottles) without identifying specific brands or types, which aligns exactly with object detection's capability to output class labels (e.g., 'product') and coordinates.
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.
✓
Object detection
Why this is correct
Object detection identifies objects and their locations with bounding boxes, which directly fulfills the requirement without needing to identify the specific product type.
Related concept
Read the scenario before looking for a memorised answer.
✗
Image classification
Why it's wrong here
Image classification assigns a label to the entire image but does not provide the location of individual objects.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts printed or handwritten text from images, not general objects.
✗
Semantic segmentation
Why it's wrong here
Semantic segmentation classifies each pixel into a predefined class (e.g., product, shelf) and typically requires a custom trained model, not a prebuilt capability.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse object detection with image classification, thinking classification can locate items, but classification only provides a single label for the whole image, not per-object positions.
Detailed technical explanation
How to think about this question
Azure Computer Vision's object detection uses a deep learning model based on the YOLO (You Only Look Once) architecture, which processes the entire image in a single pass to predict bounding boxes and class probabilities. A subtle behavior is that the prebuilt model can detect generic objects like 'bottle' or 'box' from the 80-class COCO dataset, but it may miss custom or uncommon product shapes unless fine-tuned with Custom Vision. In a real-world scenario, if shelves have overlapping products, object detection can still output separate bounding boxes with confidence scores, enabling inventory count even with partial occlusion.
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 is the correct choice because it identifies and locates multiple objects within an image by drawing bounding boxes around each detected item. For monitoring product availability on shelves, the company needs to know both the presence and position of products (e.g., boxes, bottles) without identifying specific brands or types, which aligns exactly with object detection's capability to output class labels (e.g., 'product') and coordinates.
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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