- A
Computer vision only includes OCR and text extraction from documents
Why wrong: OCR is one part of computer vision — the field encompasses much more including object detection, classification, segmentation, and facial analysis.
- B
The AI field enabling machines to interpret images and video — covering classification, detection, segmentation, and OCR
Computer vision covers all AI tasks involving visual data: classification, object detection, segmentation, OCR, facial analysis, and more.
- C
Computer vision is exclusively used for medical imaging diagnosis
Why wrong: Medical imaging is one application — computer vision is used across retail, manufacturing, security, automotive, and many other domains.
- D
The field of designing displays and cameras for computers
Why wrong: Display/camera hardware design is engineering — computer vision is an AI discipline for image and video understanding.
Quick Answer
The answer is that computer vision is the AI field enabling machines to interpret images and video, encompassing tasks like classification, detection, segmentation, and OCR. This is correct because computer vision goes beyond simple image recognition to include pixel-level analysis—image classification labels an entire scene, object detection locates and identifies multiple items, segmentation partitions images at the pixel level, and optical character recognition (OCR) extracts text from visuals. On the AI-900 exam, this concept tests your understanding of how Azure Cognitive Services map to real-world visual tasks, often appearing in scenario-based questions where you must choose the right service for a given problem. A common trap is confusing classification (one label per image) with detection (multiple objects with bounding boxes). To remember the four core tasks, think of the mnemonic “C-DOS”: Classification, Detection, Object segmentation, and OCR.
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
This AI-900 practice question tests your understanding of describe artificial intelligence workloads and considerations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
What is 'computer vision' and which tasks does it encompass?
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.
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.
- ✗
Computer vision only includes OCR and text extraction from documents
Why it's wrong here
OCR is one part of computer vision — the field encompasses much more including object detection, classification, segmentation, and facial analysis.
- ✓
The AI field enabling machines to interpret images and video — covering classification, detection, segmentation, and OCR
Why this is correct
Computer vision covers all AI tasks involving visual data: classification, object detection, segmentation, OCR, facial analysis, and more.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Computer vision is exclusively used for medical imaging diagnosis
Why it's wrong here
Medical imaging is one application — computer vision is used across retail, manufacturing, security, automotive, and many other domains.
- ✗
The field of designing displays and cameras for computers
Why it's wrong here
Display/camera hardware design is engineering — computer vision is an AI discipline for image and video understanding.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
Under the hood, computer vision models often use convolutional neural networks (CNNs) to extract hierarchical features from pixel data. For example, in object detection, architectures like YOLO (You Only Look Once) or Faster R-CNN divide the image into a grid and predict bounding boxes and class probabilities in a single forward pass. A real-world scenario where this matters is in autonomous vehicles, where simultaneous classification, detection, and segmentation of road signs, pedestrians, and lane markings must occur in real time to ensure safety.
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.
What to study next
Got this wrong? Here's your next step.
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Describe Artificial Intelligence workloads and considerations — study guide chapter
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe Artificial Intelligence workloads and considerations — This question tests Describe Artificial Intelligence workloads and considerations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
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.
About these practice questions
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Last reviewed: Jun 11, 2026
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.
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