The answer is the domain-specific models for landmark detection within Azure Computer Vision. This prebuilt capability is correct because it uses specialized, pre-trained models that can instantly recognize famous structures like the Eiffel Tower or Taj Mahal from user-uploaded photos without any custom training or labeling, directly matching the travel website’s need for a ready-to-use solution. On the AI-900 exam, this tests your understanding of the prebuilt Computer Vision services, specifically how domain-specific models differ from general image analysis or custom Vision services; a common trap is confusing this with the general “Describe image” feature, which identifies objects but not specific landmarks. To remember, think of “domain-specific” as a specialist—just as a travel guide knows landmarks, this model is pre-tuned for that exact job. A helpful memory tip is “Landmark = Domain-Specific,” since the model’s domain is famous places, not generic 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. 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.
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?
Why wrong: Image classification provides general labels (e.g., 'building', 'outdoor') but does not specifically identify landmarks like 'Eiffel Tower'.
B
Optical character recognition (OCR)
Why wrong: OCR extracts printed or handwritten text from images, not landmarks.
C
Object detection
Why wrong: Object detection identifies objects and their bounding boxes, but it is not specialized for landmark recognition without custom training.
D
Domain-specific models (Landmark detection)
Azure Computer Vision includes prebuilt domain-specific models for landmarks, allowing identification of famous landmarks without custom training.
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)
Option D is correct because 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.
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
Why it's wrong here
Image classification provides general labels (e.g., 'building', 'outdoor') but does not specifically identify landmarks like 'Eiffel Tower'.
✗
Optical character recognition (OCR)
Why it's wrong here
OCR extracts printed or handwritten text from images, not landmarks.
✗
Object detection
Why it's wrong here
Object detection identifies objects and their bounding boxes, but it is not specialized for landmark recognition without custom training.
✓
Domain-specific models (Landmark detection)
Why this is correct
Azure Computer Vision includes prebuilt domain-specific models for landmarks, allowing identification of famous landmarks without custom training.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Azure's domain-specific models for landmark detection leverage a pre-trained neural network fine-tuned on a curated dataset of thousands of global landmarks, using features like visual geometry and spatial relationships. Under the hood, the service returns a confidence score and bounding box for each detected landmark, enabling precise identification even in varied lighting or angles. In a real-world scenario, this allows the travel booking site to automatically tag photos with landmark names, enhancing user engagement without the overhead of custom model training.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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: Domain-specific models (Landmark detection) — Option D is correct because 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.
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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Question Discussion
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