- A
Organising image files into folders on Azure Blob Storage by date
Why wrong: File organisation is storage management — image classification uses AI to label images by their visual content.
- B
Assigning a category label to an entire image based on its dominant visual content
Image classification labels the whole image (cat/dog/car) — simpler than object detection, which locates specific instances within the image.
- C
Converting colour images to black and white for accessibility purposes
Why wrong: Colour conversion is image processing — classification is an AI task that assigns semantic labels to images.
- D
Sorting images by their file size and resolution metadata
Why wrong: Metadata sorting is file management — image classification uses AI to label images by their visual content and meaning.
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. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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' in Azure AI Custom Vision?
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
Assigning a category label to an entire image based on its dominant visual content
Image classification in Azure AI Custom Vision involves training a model to assign a single category label (e.g., 'dog', 'cat') to an entire image based on its dominant visual content. This is a supervised learning task where the model learns from labeled images to predict the most likely class for new, unseen images. Option B correctly describes this core functionality.
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.
- ✗
Organising image files into folders on Azure Blob Storage by date
Why it's wrong here
File organisation is storage management — image classification uses AI to label images by their visual content.
- ✓
Assigning a category label to an entire image based on its dominant visual content
Why this is correct
Image classification labels the whole image (cat/dog/car) — simpler than object detection, which locates specific instances within the image.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Converting colour images to black and white for accessibility purposes
Why it's wrong here
Colour conversion is image processing — classification is an AI task that assigns semantic labels to images.
- ✗
Sorting images by their file size and resolution metadata
Why it's wrong here
Metadata sorting is file management — image classification uses AI to label images by their visual content and meaning.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse image classification with object detection (which identifies multiple objects and their locations) or with simple image processing tasks like filtering or sorting, leading them to pick options that describe non-AI operations.
Detailed technical explanation
How to think about this question
Under the hood, Azure Custom Vision uses a convolutional neural network (CNN) architecture, typically based on ResNet or EfficientNet, which is fine-tuned via transfer learning on the user's labeled dataset. The model outputs a probability distribution across all defined classes, and the class with the highest confidence score is selected as the prediction. A real-world scenario is automated quality inspection in manufacturing, where each product image is classified as 'defective' or 'non-defective' based on visual features.
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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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: Assigning a category label to an entire image based on its dominant visual content — Image classification in Azure AI Custom Vision involves training a model to assign a single category label (e.g., 'dog', 'cat') to an entire image based on its dominant visual content. This is a supervised learning task where the model learns from labeled images to predict the most likely class for new, unseen images. Option B correctly describes this core functionality.
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
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