AI-900 Practice Question: Describe features of computer vision workloads on Azure
A museum wants to create an app that allows visitors to take a photo of a painting and receive information about the artist, year, and style. The app needs to identify the painting from a database of thousands of artworks. Which Azure Computer Vision capability is most suitable?
⚠ Common exam trap
Watch out — candidates often confuse image classification (labeling the whole image) with object detection (locating objects within the image), but the requirement to identify the painting from a photo of the entire artwork makes classification the precise fit.
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
Image classification is the correct choice because the app needs to assign a single label (the specific painting) to the entire photo. Azure Computer Vision's image classification models are trained to recognize and categorize entire images into predefined classes, which matches the requirement of identifying a painting from a database of thousands of artworks based on the visual content of the photo.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Optical Character Recognition (OCR)
Why it's wrong here
Optical Character Recognition (OCR) is a technology that extracts printed or handwritten text from images and converts it into machine-readable characters. While a painting might contain a signature or title, OCR would only return that textual content, not the painting's identity, creator, style, or subject matter. It cannot analyze visual features like composition, color palette, or brushwork, which are essential for recognizing a specific artwork. Therefore, OCR is unsuitable because it addresses textual data rather than whole-image content.
- ✓
Image classification
Why this is correct
Image classification is the correct approach because it analyzes the entire image as a single unit and assigns it a label from a predefined set of categories. In this scenario, the model could be trained on a dataset where each painting is its own class, allowing the app to map a visitor's photo to the specific artwork. It uses learned visual patterns—such as overall structure, color distribution, and distinctive motifs—to make the prediction. This directly matches the goal of identifying the painting itself, not just some element within it.
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Object detection
Why it's wrong here
Object detection is designed to locate and classify multiple distinct objects within an image by drawing bounding boxes around each one. For a museum app, object detection might pick out individual figures, vases, or architectural elements inside a painting, but it would not identify the painting as a whole. The task here is to recognize the entire artwork from a photo, which is a single-label, whole-image problem rather than a multi-object localization problem. Using object detection would return irrelevant component boxes and fail to provide the artwork's identity as the primary output.
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Face detection
Why it's wrong here
Face detection specifically identifies and localizes human faces in an image, often for tasks like face counting or recognition. Many paintings, especially portraits, contain faces, so a face detector would fire on them, but that output conveys nothing about which painting was photographed or its artist. The app requires classifying the entire image to determine the artwork's identity, style, and historical context—information that face detection cannot provide. It is a specialized feature detection task, not a general image-understanding method suitable for this use case.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Label
A label is a piece of metadata attached to data, objects, or resources to identify, classify, or describe them for easier management and retrieval.
Key term
Computer vision
Computer vision is a field of artificial intelligence that enables computers to interpret and make decisions based on visual data from the world, such as images and videos.
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