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 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?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Image Analysis (object detection)
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.
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 Analysis (object detection)
Why this is correct
Object detection can locate multiple instances of objects (e.g., boxes) and provide properties like color, enabling counting and attribute extraction.
Related concept
Read the scenario before looking for a memorised answer.
✗
Optical Character Recognition (OCR)
Why it's wrong here
OCR extracts printed or handwritten text from images, not objects or their colors.
✗
Face detection
Why it's wrong here
Face detection is used to locate and identify human faces, not inanimate objects like boxes.
✗
Spatial analysis
Why it's wrong here
Spatial analysis processes video to track movement and presence of people, not static object counting in images.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse object detection with OCR or spatial analysis, thinking text extraction or motion tracking could somehow count boxes or detect colors, when in fact object detection is the only option that can both localize objects and support color analysis.
Detailed technical explanation
How to think about this question
Under the hood, Azure Image Analysis object detection uses deep neural networks (e.g., ResNet-based models) to output bounding boxes with confidence scores and class labels. For color detection, you would need to analyze the pixel values within each bounding box (e.g., using HSV color space thresholds) since the built-in object detection does not natively output object color; this is a common post-processing step in real-world logistics solutions.
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: Image Analysis (object detection) — 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.
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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