Quick Answer
The answer is object detection because it is the only Azure Computer Vision capability that can both identify and locate multiple objects within a single image or video frame, making it essential for counting boxes on a conveyor belt. Unlike image classification, which assigns a single label to an entire scene, object detection provides bounding boxes around each individual box and returns a count, enabling real-time throughput tracking. On the AI-900 exam, this scenario tests your understanding of the core difference between image classification and object detection—a common trap is choosing classification when the task requires counting or locating multiple items. Remember the memory tip: if you need to count, you need to detect; classification labels the whole picture, but detection finds each piece.
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 warehouse uses video cameras to monitor a conveyor belt. They need to count the number of boxes passing by each hour to track throughput. Which Azure Computer Vision capability should they use?
Optical Character Recognition (OCR)
Why wrong: OCR extracts text from images, not for counting objects.
Face Detection
Why wrong: Face Detection identifies human faces, not boxes.
Image Classification
Why wrong: Image Classification labels the entire image with one category; it does not detect or count individual objects.
Object Detection
Object Detection finds and locates multiple objects in an image/video, enabling counting of boxes.
Answer choices
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 capability because it can identify and locate multiple boxes within each video frame, allowing the system to count them as they move along the conveyor belt. Unlike image classification, which labels an entire image, object detection provides bounding boxes and counts for each detected object, making it ideal for real-time throughput tracking.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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 text from images, not for counting objects.
Face Detection
Why it's wrong here
Face Detection identifies human faces, not boxes.
Image Classification
Why it's wrong here
Image Classification labels the entire image with one category; it does not detect or count individual objects.
Object Detection
Why this is correct
Object Detection finds and locates multiple objects in an image/video, enabling counting of boxes.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates confuse Image Classification with Object Detection, assuming that classifying an image as 'box' is sufficient, but classification cannot count multiple objects or provide their locations.
Detailed technical explanation
Azure Computer Vision's Object Detection API uses deep learning models like YOLO or Faster R-CNN to output bounding box coordinates and confidence scores for each detected object. In a conveyor belt scenario, the system can track boxes across frames using intersection-over-union (IoU) matching to avoid double-counting, enabling accurate per-hour throughput metrics. This capability is also used in retail for shelf inventory counting and in manufacturing for defect detection.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
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..
The correct answer is: Object Detection — Object Detection is the correct capability because it can identify and locate multiple boxes within each video frame, allowing the system to count them as they move along the conveyor belt. Unlike image classification, which labels an entire image, object detection provides bounding boxes and counts for each detected object, making it ideal for real-time throughput tracking.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Same concept, more angles
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 warehouse uses a camera system to locate and count boxes on shelves. The system needs to output the exact positions of each box by drawing a rectangular frame around it in the image. Which Azure Computer Vision capability should they use?
mediumWhy A: Object detection is the correct capability because it identifies and localizes multiple objects within an image by drawing bounding boxes around each detected instance. In this scenario, the system needs to locate and count individual boxes on shelves, which requires both classification (what is a box) and localization (where each box is), exactly what object detection provides.
Variation 2. A logistics company uses security cameras to monitor boxes on warehouse shelves. They need an AI solution that can count the number of boxes on each shelf and also identify if any box is red (indicating a priority shipment). Which Azure Computer Vision capability should they use?
mediumWhy A: Option A is correct because Image Analysis with object detection can identify and localize multiple objects (boxes) within an image, count them, and detect specific attributes like color (red boxes) by analyzing pixel values in the detected bounding boxes. This directly meets the requirement to count boxes and identify priority shipments based on color.
Variation 3. A retail chain uses ceiling-mounted cameras to monitor shelf inventory. They need to identify and locate individual products (e.g., a specific brand of cereal) within an image and count how many are present. Which Azure Computer Vision capability should they use?
mediumWhy B: Object detection is the correct capability because it not only identifies the presence of a specific product (e.g., a brand of cereal) within an image but also localizes each instance by drawing bounding boxes around them, enabling an accurate count. Image classification would only label the entire image as containing cereal without locating individual boxes, while OCR and semantic segmentation serve different purposes (text extraction and pixel-level labeling, respectively).
Variation 4. A city transportation department wants to use a live camera feed at a bus stop to estimate how many people are waiting for the bus. Which Azure Computer Vision capability should they use?
easyWhy C: Object detection is the correct capability because it can identify and locate multiple people in a live camera feed, providing bounding boxes around each person. This allows the system to count the number of individuals waiting at the bus stop, which is the core requirement. Optical Character Recognition (OCR) extracts text, face detection identifies faces but not necessarily counts people in a crowd, and semantic segmentation classifies each pixel but is overkill for simple counting.
Last reviewed: Jun 11, 2026
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