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 chain wants to automatically detect which specific products are missing from store shelves by analyzing images from in-store cameras. Each product has a distinct shape and label. Which Azure Computer Vision capability is most appropriate for this task?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
B) Object Detection
Object Detection (Option B) is the correct choice because it can identify and locate multiple products within an image by drawing bounding boxes around each detected object. This allows the system to determine which specific products are missing by comparing detected items against an expected inventory list. Image Classification would only label the entire image, not individual products, while OCR focuses on text extraction and Facial Recognition identifies people.
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.
✗
A) Image Classification
Why it's wrong here
Image classification assigns a single label to an entire image, so it cannot identify multiple specific products in one scene.
✓
B) Object Detection
Why this is correct
Correct. Object detection can locate and label multiple objects (products) within an image, allowing detection of missing items.
Related concept
Read the scenario before looking for a memorised answer.
✗
C) Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts text from images, but cannot determine the presence or absence of physical products.
✗
D) Facial Recognition
Why it's wrong here
Facial recognition is designed to detect and identify human faces, not products on shelves.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Image Classification with Object Detection, thinking that classifying the entire image as 'shelf with products' is sufficient, but the task requires locating and identifying individual missing products, which only Object Detection can do.
Detailed technical explanation
How to think about this question
Object Detection in Azure Computer Vision uses deep learning models like YOLO (You Only Look Once) or Faster R-CNN to output bounding boxes and class labels for each object. The model is trained on a dataset of product images with annotated bounding boxes, enabling it to handle variations in lighting, occlusion, and perspective. In a real-world scenario, the system could trigger an alert when the count of a specific product bounding box falls below a threshold, enabling automated restocking.
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: B) Object Detection — Object Detection (Option B) is the correct choice because it can identify and locate multiple products within an image by drawing bounding boxes around each detected object. This allows the system to determine which specific products are missing by comparing detected items against an expected inventory list. Image Classification would only label the entire image, not individual products, while OCR focuses on text extraction and Facial Recognition identifies people.
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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