The answer is object detection. This is the correct choice because object detection identifies and locates multiple objects within an image using bounding boxes and class labels, allowing the system to pinpoint exactly where a product is missing from its expected shelf position. Image classification, by contrast, would only assign a single label to the entire camera frame, such as “full shelf” or “empty shelf,” without providing the precise location of gaps. On the Microsoft Azure AI Fundamentals AI-900 exam, this distinction tests your understanding of how Azure Computer Vision capabilities map to real-world business scenarios—a common trap is confusing object detection with image classification when the task requires spatial awareness of multiple items. Remember the memory tip: “Classification tells you what, detection tells you where and what.”
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 uses ceiling-mounted cameras to monitor shelf stock. They want an automated system that analyzes each camera image to detect if any product is missing from its expected location on the shelf (a product gap). Which Azure Computer Vision capability should they use?
Why wrong: Image classification assigns a label to the entire image (e.g., 'full shelf' vs 'empty shelf'), but does not locate individual products to identify gaps.
B
Optical Character Recognition (OCR)
Why wrong: OCR extracts text from images, but product gaps do not involve text.
C
Object detection
Object detection finds and locates objects within an image. By detecting the expected products, the system can determine if any are missing, indicating a gap.
D
Face detection
Why wrong: Face detection locates human faces, which is irrelevant to detecting product gaps on shelves.
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 can identify and locate multiple objects (e.g., product boxes) within an image and determine if expected items are missing from their designated positions on the shelf. Unlike image classification, which assigns a single label to the entire image, object detection provides bounding boxes and class labels for each detected object, enabling precise gap analysis.
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.
✗
Image classification
Why it's wrong here
Image classification assigns a label to the entire image (e.g., 'full shelf' vs 'empty shelf'), but does not locate individual products to identify gaps.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts text from images, but product gaps do not involve text.
✓
Object detection
Why this is correct
Object detection finds and locates objects within an image. By detecting the expected products, the system can determine if any are missing, indicating a gap.
Related concept
Read the scenario before looking for a memorised answer.
✗
Face detection
Why it's wrong here
Face detection locates human faces, which is irrelevant to detecting product gaps on shelves.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse image classification (which labels the whole scene) with object detection (which locates individual objects), leading them to choose option A when the task requires spatial awareness of multiple items.
Detailed technical explanation
How to think about this question
Under the hood, Azure Computer Vision object detection uses deep learning models (e.g., YOLO or Faster R-CNN) to output bounding boxes and confidence scores for each detected product class. The system can be trained on labeled shelf images to recognize specific products, and a gap is inferred when an expected bounding box region contains no detection above a confidence threshold. In a real-world scenario, the model must handle variations in lighting, occlusion, and product packaging to avoid false positives or missed gaps.
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 can identify and locate multiple objects (e.g., product boxes) within an image and determine if expected items are missing from their designated positions on the shelf. Unlike image classification, which assigns a single label to the entire image, object detection provides bounding boxes and class labels for each detected object, enabling precise gap analysis.
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.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A retail company uses security cameras to monitor shelves. They want to identify whether a customer is holding a specific product (e.g., a green detergent bottle) and also determine the location of that product within the camera frame. Which Azure Computer Vision capability should they use?
medium
✓ A.Object detection
B.Image classification
C.Optical character recognition (OCR)
D.Semantic segmentation
Why A: Object detection is the correct capability because it not only identifies the presence of a specific product (like a green detergent bottle) in an image but also returns bounding box coordinates that indicate the product's location within the camera frame. This dual output—classification plus localization—directly matches the requirement to both recognize the object and determine its position.
Variation 2. A security company needs to monitor a warehouse using video cameras. They want to detect whether any persons are present in a given frame and also know their approximate locations. Which Azure Computer Vision capability should they use?
medium
A.Image classification
✓ B.Object detection
C.Semantic segmentation
D.Optical Character Recognition (OCR)
Why B: Object detection is the correct choice because it not only identifies whether persons are present in a video frame but also provides bounding box coordinates indicating their approximate locations. This capability is specifically designed to locate multiple objects of interest within an image, which directly matches the requirement of detecting persons and knowing where they are.
Variation 3. What is object detection, and how does it differ from image classification?
medium
A.Object detection identifies what is in an image; image classification also identifies where objects are located
✓ B.Object detection identifies and locates multiple objects with bounding boxes; image classification labels the whole image
C.Object detection and image classification are the same task
D.Object detection is used only for face recognition
Why B: Object detection goes beyond image classification by not only identifying what objects are present in an image but also localizing each object with a bounding box. Image classification assigns a single label to the entire image, whereas object detection can handle multiple objects of different classes simultaneously. This makes object detection suitable for tasks like counting objects or tracking their positions.
Variation 4. What is image classification and how is it different from object detection?
medium
✓ A.Image classification labels the whole image; object detection finds and locates multiple objects within it
B.Image classification is faster; object detection is slower but more accurate
C.Image classification works on videos; object detection works on static images only
D.They are the same task with different names
Why A: Image classification assigns a single label to an entire image based on its dominant content, such as 'cat' or 'dog'. Object detection goes further by not only identifying multiple objects within an image but also drawing bounding boxes around each one, providing both class labels and spatial locations. This distinction is fundamental in computer vision workloads on Azure, where Custom Vision and Computer Vision API offer separate capabilities for classification and detection tasks.
Last reviewed: Jun 11, 2026
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