AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
What is 'computer vision' and which tasks does it encompass?
⚠ Common exam trap
The trap here is that candidates often associate computer vision solely with OCR or medical imaging due to common use cases, but the exam expects recognition of its full task range including classification, detection, and segmentation.
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
✓
The AI field enabling machines to interpret images and video — covering classification, detection, segmentation, and OCR
Computer vision is a subfield of AI that enables machines to derive meaningful information from digital images, videos, and other visual inputs. It encompasses a broad range of tasks including image classification (labeling an entire image), object detection (locating and classifying multiple objects), image segmentation (pixel-level partitioning), and optical character recognition (OCR) for text extraction. Option B correctly captures this full scope, making it the right answer.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Computer vision only includes OCR and text extraction from documents
Why it's wrong here
OCR is a narrow subfield of computer vision focused specifically on extracting machine-readable text from images and scanned documents. Computer vision is far broader, encompassing image classification (labeling an entire image), object detection (locating instances of objects), semantic/instance segmentation (pixel-level understanding), facial recognition, and pose estimation, among others. Reducing computer vision to OCR ignores the wide range of perceptual tasks it addresses.
- ✓
The AI field enabling machines to interpret images and video — covering classification, detection, segmentation, and OCR
Why this is correct
This is the accurate definition of computer vision: an AI subfield that enables machines to derive meaningful information from digital images, videos, and other visual inputs. It encompasses a range of core tasks—including image classification (assigning a label to the whole image), object detection (localizing and identifying objects), semantic segmentation (labeling every pixel), and OCR (extracting text). These capabilities underpin modern applications such as autonomous driving, surveillance, and augmented reality.
- ✗
Computer vision is exclusively used for medical imaging diagnosis
Why it's wrong here
While computer vision is indeed a critical tool in medical imaging—such as automating tumor detection from MRI or CT scans—that is only one vertical application. The technology is deployed across retail (inventory tracking, cashier-less stores), manufacturing (defect detection on assembly lines), security (face recognition for access control), and automotive (object avoidance in self-driving vehicles), among many other sectors. Confining computer vision to healthcare disregards its versatile, industry-agnostic nature.
- ✗
The field of designing displays and cameras for computers
Why it's wrong here
Designing displays and cameras is the discipline of optical and hardware engineering, concerned with the physical components that capture or render visual media. Computer vision is an AI discipline focused on the software algorithms that analyze and interpret the captured visual data, such as recognizing objects or estimating depth. Confusing the two conflates the sensing hardware with the cognitive processing layer that gives machines 'sight.'
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Image classification
Image classification is the process of teaching a computer to look at a picture and decide what category or label best describes the main object or scene in that picture.
Key term
Object detection
Object detection is a computer vision technology that identifies and locates specific objects within an image or video.
About these practice questions
This AI-900 question is part of Courseiva's 985-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.