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 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?
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 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.
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.
✓
Object detection
Why this is correct
Object detection finds objects and returns their bounding boxes, which is precisely what is needed to locate and frame each box in an image.
Related concept
Read the scenario before looking for a memorised answer.
✗
Image classification
Why it's wrong here
Image classifies the entire image into a category (e.g., 'shelf with boxes') but does not locate individual objects within the image.
✗
Semantic segmentation
Why it's wrong here
Semantic segmentation assigns a class label to each pixel, creating a pixel‑level mask rather than rectangular bounding boxes.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts text from images, not the positions of physical objects like boxes.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse semantic segmentation with object detection because both involve 'segments' or 'regions,' but segmentation does not separate individual instances of the same object type, making it unsuitable for counting distinct boxes.
Detailed technical explanation
How to think about this question
Object detection in Azure Computer Vision uses deep learning models like YOLO or Faster R-CNN, which output a list of detected objects with bounding box coordinates (x, y, width, height) and confidence scores. The API returns results in JSON format, allowing the system to count boxes by iterating over detected instances and drawing rectangles programmatically. A subtle behavior is that overlapping boxes may be merged or suppressed via non-maximum suppression to avoid duplicate detections.
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 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.
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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