- A
Image classification labels the whole image; object detection finds and locates multiple objects within it
Classification = one label for whole image; object detection = multiple objects each with class label and bounding box coordinates.
- B
Image classification is faster; object detection is slower but more accurate
Why wrong: While object detection is more complex, the key difference is what they output — not just speed.
- C
Image classification works on videos; object detection works on static images only
Why wrong: Both can work on images or video frames — the distinction is what information they return.
- D
They are the same task with different names
Why wrong: They are fundamentally different tasks — classification assigns one image-level label; detection localizes multiple objects.
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. 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 image classification and how is it different from object detection?
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
Image classification labels the whole image; object detection finds and locates multiple objects within it
Image classification assigns a single label to an entire image based on its dominant content, such as 'cat' or 'dog'. Object detection goes further by not only identifying multiple objects within an image but also drawing bounding boxes around each one, providing both class labels and spatial locations. This distinction is fundamental in computer vision workloads on Azure, where Custom Vision and Computer Vision API offer separate capabilities for classification and detection tasks.
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 classification labels the whole image; object detection finds and locates multiple objects within it
Why this is correct
Classification = one label for whole image; object detection = multiple objects each with class label and bounding box coordinates.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Image classification is faster; object detection is slower but more accurate
Why it's wrong here
While object detection is more complex, the key difference is what they output — not just speed.
- ✗
Image classification works on videos; object detection works on static images only
Why it's wrong here
Both can work on images or video frames — the distinction is what information they return.
- ✗
They are the same task with different names
Why it's wrong here
They are fundamentally different tasks — classification assigns one image-level label; detection localizes multiple objects.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the output granularity—thinking object detection is just a 'more detailed' version of classification rather than a fundamentally different task with spatial localization, leading them to choose Option B or D.
Trap categories for this question
Command / output trap
While object detection is more complex, the key difference is what they output — not just speed.
Detailed technical explanation
How to think about this question
Under the hood, image classification typically uses convolutional neural networks (CNNs) like ResNet or EfficientNet that output a single probability distribution over classes via a softmax layer. Object detection models such as YOLO, Faster R-CNN, or SSD employ region proposal networks or anchor boxes to predict bounding box coordinates and class probabilities for multiple objects simultaneously. In Azure, the Custom Vision service allows you to train both types of models, and the Computer Vision API's 'Analyze Image' operation returns tags (classification) while 'Detect Objects' returns bounding boxes and labels.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Describe features of computer vision workloads on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe features of computer vision workloads on Azure practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this AI-900 question test?
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 classification labels the whole image; object detection finds and locates multiple objects within it — Image classification assigns a single label to an entire image based on its dominant content, such as 'cat' or 'dog'. Object detection goes further by not only identifying multiple objects within an image but also drawing bounding boxes around each one, providing both class labels and spatial locations. This distinction is fundamental in computer vision workloads on Azure, where Custom Vision and Computer Vision API offer separate capabilities for classification and detection tasks.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More AI-900 practice questions
- A company deploys an AI system to screen job applications. The system is a complex neural network that learns patterns f…
- What is 'model versioning' and why is it essential in MLOps?
- What is 'AI transparency' in Microsoft's Responsible AI principles?
- A company uses Azure OpenAI Service to generate marketing copy. They notice that sometimes the generated text contains r…
- A data scientist is training a regression model to predict house prices using features like square footage, number of be…
- A company uses Azure OpenAI Service to generate marketing copy. They want to ensure that the generated text does not con…
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.