The answer is object detection. This is the correct choice because object detection is specifically designed to identify the presence and location of multiple objects—such as forklifts and pallets—within an image or video frame, returning bounding box coordinates for each detected item. For a warehouse inventory monitoring use case requiring real-time spatial awareness from video feeds, this capability directly meets the need to know both what objects are present and exactly where they are positioned. On the Microsoft Azure AI-900 exam, this question tests your ability to distinguish between Computer Vision services: object detection (which provides location data) versus image classification (which only labels the whole scene) or semantic segmentation (which labels every pixel). A common trap is confusing object detection with image classification—remember that if the task requires bounding boxes or coordinates, it is object detection. Memory tip: think "detect and locate" for object detection, as it answers both "what" and "where."
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 warehouse uses AI to monitor inventory. They need to detect the presence and location of specific objects (e.g., forklifts, pallets) in real-time video feeds. Which Azure Computer Vision capability should they use?
Why wrong: Image classification assigns a label to the entire image (e.g., 'warehouse'), but does not provide location information for individual objects.
B
OCR (optical character recognition)
Why wrong: OCR extracts text from images, not applicable for detecting non-text objects like forklifts.
C
Object detection
Object detection identifies multiple objects within an image and returns their bounding boxes and class labels, perfect for locating forklifts and pallets in warehouse video.
D
Facial recognition
Why wrong: Facial recognition is specialized for identifying human faces, not relevant for detecting forklifts or pallets.
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 specific objects (e.g., forklifts, pallets) within an image or video frame and returns bounding box coordinates indicating their location. This capability is designed for real-time spatial awareness, which directly matches the warehouse's need to detect both the presence and position of objects in video feeds.
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., 'warehouse'), but does not provide location information for individual objects.
✗
OCR (optical character recognition)
Why it's wrong here
OCR extracts text from images, not applicable for detecting non-text objects like forklifts.
✓
Object detection
Why this is correct
Object detection identifies multiple objects within an image and returns their bounding boxes and class labels, perfect for locating forklifts and pallets in warehouse video.
Related concept
Read the scenario before looking for a memorised answer.
✗
Facial recognition
Why it's wrong here
Facial recognition is specialized for identifying human faces, not relevant for detecting forklifts or pallets.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse image classification (which only labels the whole scene) with object detection (which locates individual objects), especially when the question emphasizes 'presence and location' — a classic AI-900 pitfall.
Detailed technical explanation
How to think about this question
Azure's object detection uses deep learning models like YOLO (You Only Look Once) or Faster R-CNN, which process video frames in real time to output bounding boxes and class probabilities. A subtle behavior is that the model can detect multiple instances of the same object class (e.g., several pallets) and handles occlusion by predicting confidence scores, which helps filter low-confidence detections in cluttered warehouse scenes.
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 specific objects (e.g., forklifts, pallets) within an image or video frame and returns bounding box coordinates indicating their location. This capability is designed for real-time spatial awareness, which directly matches the warehouse's need to detect both the presence and position of objects in video feeds.
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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