Describe Artificial Intelligence workloads and considerations →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
What is computer vision and give three real-world application examples.
⚠ Common exam trap
Many candidates assume computer vision is narrowly defined (e.g., only for text or satellite imagery) or that it requires prohibitively expensive hardware, when in fact it is a broad, cloud-accessible technology with many practical applications.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Computer vision enables machines to understand visual data — used in autonomous driving, medical imaging, and retail automation
Computer vision is a field of AI that enables machines to interpret and make decisions based on visual data from the world, such as images and videos. The three examples given—autonomous driving (e.g., detecting pedestrians and lane markings), medical imaging (e.g., analyzing X-rays for tumors), and retail automation (e.g., self-checkout systems recognizing products)—are classic real-world applications that demonstrate the breadth of computer vision beyond simple text recognition.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Computer vision is limited to text recognition only; it cannot detect objects
Why it's wrong here
Computer vision is not limited to text recognition. Beyond OCR, it handles object detection, image classification, semantic segmentation, pose estimation, and even activity recognition in videos. For instance, Azure's Computer Vision includes Image Analysis APIs for tags, captions, and brand detection, plus specialized tools for face detection and spatial analysis. Limiting computer vision to OCR ignores its core purpose of general visual comprehension and the rich feature set available in Azure AI.
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Computer vision enables machines to understand visual data — used in autonomous driving, medical imaging, and retail automation
Why this is correct
This accurately describes computer vision, which enables machines to extract meaning from visual data. In autonomous driving, vision systems identify lane markings, traffic signs, and pedestrians in real time; in medical imaging, deep learning models delineate tumors or pathologies from MRI and CT scans; and in retail automation, vision tracks products and customers for self-checkout or smart inventory. Azure provides pre-built computer vision services and custom model training through Cognitive Services, making these capabilities accessible without building algorithms from scratch.
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Computer vision only works on satellite imagery for geographic analysis
Why it's wrong here
The statement that computer vision only works on satellite imagery is incorrect. Computer vision is a general-purpose AI technology that interprets any visual input, including digital photos, videos, camera feeds, and even medical scans. While satellite imagery is a legitimate application, the same algorithms are widely used for facial recognition, autonomous navigation, object detection in manufacturing, and retail analytics to name a few. Azure's Computer Vision APIs are domain-agnostic and accept various common image formats for analysis.
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Computer vision requires extremely expensive hardware unavailable in the cloud
Why it's wrong here
The claim that computer vision requires extremely expensive hardware unavailable in the cloud is false. Azure offers GPU-accelerated virtual machines and managed cognitive services that handle computer vision workloads without any on-premises hardware. Users can train and deploy models via pay-as-you-go cloud resources, and many inference tasks even run efficiently on CPUs or cost-efficient Azure AI services. Thus, cloud availability makes computer vision accessible at any scale.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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