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 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?
Why wrong: Image classification would only tell if a person is present in the image, but cannot provide the location of each person.
B
Object detection
Object detection identifies multiple objects of interest and provides bounding box coordinates, exactly what is needed to know that persons are present and where they are located.
C
Semantic segmentation
Why wrong: Semantic segmentation labels every pixel in the image, which is overkill for simple detection and location; object detection is more efficient for bounding boxes around persons.
D
Optical Character Recognition (OCR)
Why wrong: OCR extracts printed or handwritten text from images, not relevant to detecting persons.
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 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.
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 would only tell if a person is present in the image, but cannot provide the location of each person.
✓
Object detection
Why this is correct
Object detection identifies multiple objects of interest and provides bounding box coordinates, exactly what is needed to know that persons are present and where they are located.
Related concept
Read the scenario before looking for a memorised answer.
✗
Semantic segmentation
Why it's wrong here
Semantic segmentation labels every pixel in the image, which is overkill for simple detection and location; object detection is more efficient for bounding boxes around persons.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts printed or handwritten text from images, not relevant to detecting persons.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse object detection with image classification, thinking that simply labeling an image as containing a person is sufficient, but the question explicitly requires 'approximate locations' which only object detection provides.
Detailed technical explanation
How to think about this question
Under the hood, Azure Computer Vision's object detection uses deep neural networks (e.g., YOLO or Faster R-CNN) that output bounding boxes with confidence scores for each detected object class. A subtle behavior is that object detection can return multiple instances of the same class (e.g., two persons) with separate bounding boxes, whereas semantic segmentation would merge them into a single 'person' region. In a real-world warehouse scenario, object detection allows the security system to count individuals and track their movements across frames.
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 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.
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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