- A
Computer vision is limited to text recognition only; it cannot detect objects
Why wrong: Computer vision encompasses much more than text recognition — it includes object detection, image classification, segmentation, and more.
- B
Computer vision enables machines to understand visual data — used in autonomous driving, medical imaging, and retail automation
Computer vision interprets images/video for tasks like autonomous vehicle navigation, medical diagnosis support, and automated checkout.
- C
Computer vision only works on satellite imagery for geographic analysis
Why wrong: Computer vision applies to any image/video — it's not limited to satellite imagery.
- D
Computer vision requires extremely expensive hardware unavailable in the cloud
Why wrong: Azure provides GPU-accelerated cloud computing for computer vision — specialized hardware is not required on-premises.
Quick Answer
The correct answer is that computer vision enables machines to understand and interpret visual data from the world, with classic real-world examples including autonomous driving, medical imaging, and retail automation. This field of AI works by training models to extract meaningful information from images and videos, allowing systems to detect objects, recognize patterns, and make decisions based on what they "see." On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your grasp of core AI workloads, often appearing alongside other vision tasks like image classification or optical character recognition. A common trap is confusing computer vision with simple text recognition or image storage—remember that true computer vision involves interpretation and decision-making, not just capturing or storing pictures. To recall the three key examples, think of the mnemonic "ARM": Autonomous driving, Retail automation, and Medical imaging.
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 give three real-world application examples.
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
Option B is correct because 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.
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 is limited to text recognition only; it cannot detect objects
Why it's wrong here
Computer vision encompasses much more than text recognition — it includes object detection, image classification, segmentation, and more.
- ✓
Computer vision enables machines to understand visual data — used in autonomous driving, medical imaging, and retail automation
Why this is correct
Computer vision interprets images/video for tasks like autonomous vehicle navigation, medical diagnosis support, and automated checkout.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Computer vision only works on satellite imagery for geographic analysis
Why it's wrong here
Computer vision applies to any image/video — it's not limited to satellite imagery.
- ✗
Computer vision requires extremely expensive hardware unavailable in the cloud
Why it's wrong here
Azure provides GPU-accelerated cloud computing for computer vision — specialized hardware is not required on-premises.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
Under the hood, computer vision systems often use convolutional neural networks (CNNs) to extract hierarchical features from pixel data, such as edges, textures, and shapes, which are then used for tasks like object detection (e.g., YOLO or Faster R-CNN) or image segmentation. A subtle behavior is that these models are highly sensitive to training data distribution; for example, a model trained on daytime driving scenes may fail in low-light conditions unless augmented with diverse datasets. In real-world scenarios like autonomous driving, this means continuous retraining and sensor fusion (e.g., combining camera data with LiDAR) are critical for 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
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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: Computer vision enables machines to understand visual data — used in autonomous driving, medical imaging, and retail automation — Option B is correct because 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.
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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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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