AI-900 Practice Question: Describe features of computer vision workloads on Azure
A travel booking website wants to automatically identify famous landmarks (e.g., Eiffel Tower, Taj Mahal) in photos uploaded by users. They want to use a prebuilt Azure Computer Vision feature without custom training. Which capability should they use?
⚠ Common exam trap
It's easy for candidates to confuse object detection (which locates generic objects) with domain-specific models (which are pre-trained for specialized tasks like landmark recognition), leading them to choose Option C incorrectly.
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
✓
Domain-specific models (Landmark detection)
Azure Computer Vision includes prebuilt domain-specific models for landmark detection that can identify famous landmarks like the Eiffel Tower or Taj Mahal without any custom training. This capability is specifically designed to recognize well-known structures from user-uploaded photos, making it the ideal choice for the travel booking website's requirement.
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
Why it's wrong here
General image classification analyzes the dominant content of an entire image and assigns broad labels or tags such as 'building', 'tower', or 'outdoor'. While such labels may describe the scene, they are not grounded in a specific landmark catalog, so they cannot authoritatively say 'Eiffel Tower' versus 'Leaning Tower of Pisa'. Additionally, image classification lacks the fine-grained, one-vs-many recognition that landmark identification requires, meaning the travel site would need to train a custom classifier and label an entire dataset to get the desired landmark name.
When this WOULD be correct
A question asking for a prebuilt Azure Computer Vision feature to categorize images into broad categories (e.g., 'beach', 'mountain', 'city') without needing to identify specific landmarks or objects would make image classification correct.
- ✗
Optical character recognition (OCR)
Why it's wrong here
Optical character recognition extracts printed or handwritten text from images, not the visual features of landmarks such as shape, colour, or spatial structure. It is tempting because OCR can read signs or labels on landmarks, but the scenario requires identifying the landmark itself from its visual appearance, not reading any text present. OCR would be correct if the task were to digitise text from a photo of a document or a sign.
When this WOULD be correct
A question asking to extract printed or handwritten text from images, such as reading license plates or signs, would make OCR the correct choice.
- ✗
Object detection
Why it's wrong here
Object detection uses a prebuilt vocabulary of common objects and returns one or more bounding boxes with class labels, such as 'person', 'vehicle', or 'building'. When applied to a travel photo, it might draw a box around a generic tower or an architectural feature, but the prebuilt model is not seeded with famous-landmark identities, so it cannot determine which known landmark is present. Detecting multiple boxes also requires extra heuristics to decide which one is the landmark, and the output does not provide the specific, recognizable name needed by the travel booking website.
When this WOULD be correct
Object detection would be correct if the question asked for identifying and locating multiple types of objects (e.g., cars, people, animals) in an image, or if the requirement was to draw bounding boxes around generic objects without needing to recognize specific landmarks.
- ✓
Domain-specific models (Landmark detection)
Why this is correct
Azure Computer Vision provides a prebuilt, domain-specific model for landmark detection that is trained on a large catalog of globally famous structures such as the Eiffel Tower and Statue of Liberty. When invoked with the Analyze Image API, this model not only recognizes the landmark but also returns its canonical name, a confidence score, and sometimes a bounding box. Because the model is already specialized, the travel website can identify famous landmarks directly without building or training a custom model.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Domain-specific models (Landmark detection)Correct answer▾
Why this is correct
Azure Computer Vision provides a prebuilt, domain-specific model for landmark detection that is trained on a large catalog of globally famous structures such as the Eiffel Tower and Statue of Liberty. When invoked with the Analyze Image API, this model not only recognizes the landmark but also returns its canonical name, a confidence score, and sometimes a bounding box. Because the model is already specialized, the travel website can identify famous landmarks directly without building or training a custom model.
✗Image classificationWrong answer — click to see why▾
Why this is wrong here
Image classification assigns a single label to an entire image, but the requirement is to identify specific landmarks within photos, which requires recognizing multiple distinct objects or scenes. Prebuilt landmark detection is a specialized domain-specific model, not general image classification.
★ When this WOULD be the correct answer
A question asking for a prebuilt Azure Computer Vision feature to categorize images into broad categories (e.g., 'beach', 'mountain', 'city') without needing to identify specific landmarks or objects would make image classification correct.
Why candidates choose this
Candidates may confuse 'identifying landmarks' with 'classifying images' because both involve labeling, but they overlook that landmark detection is a specialized subcategory of image classification tailored for famous places.
✗Optical character recognition (OCR)Wrong answer — click to see why▾
Why this is wrong here
OCR extracts text from images, but the question asks for identifying landmarks, which are visual objects, not text.
★ When this WOULD be the correct answer
A question asking to extract printed or handwritten text from images, such as reading license plates or signs, would make OCR the correct choice.
Why candidates choose this
Candidates may confuse OCR with general image analysis, thinking it can recognize landmarks if they have text labels, but OCR only reads text, not objects.
✗Object detectionWrong answer — click to see why▾
Why this is wrong here
Object detection identifies and locates objects within an image, but it does not specifically recognize famous landmarks. The prebuilt Computer Vision service includes a dedicated domain-specific model for landmark detection, which is optimized for this task.
★ When this WOULD be the correct answer
Object detection would be correct if the question asked for identifying and locating multiple types of objects (e.g., cars, people, animals) in an image, or if the requirement was to draw bounding boxes around generic objects without needing to recognize specific landmarks.
Why candidates choose this
Candidates may confuse object detection with landmark detection because both involve identifying items in images, and they might think that detecting landmarks is a subset of object detection, not realizing Azure offers a specialized prebuilt model for landmarks.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
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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.
Key term
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
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