- A
Detecting important milestones in a project timeline using AI
Why wrong: Project milestones are project management — landmark detection identifies physical world famous monuments and natural features.
- B
Identifying well-known physical landmarks (Eiffel Tower, Big Ben) in photographs with a confidence score
Landmark detection names famous locations from photos — enabling automatic location tagging and travel content analysis.
- C
Creating highlighted markers on maps showing user-defined points of interest
Why wrong: Map markers are GIS tools — landmark detection identifies famous locations in existing photos using AI.
- D
Detecting major architectural features of any building regardless of whether it is famous
Why wrong: Generic architectural analysis is part of image captioning — landmark detection specifically recognises globally famous named locations.
Quick Answer
The answer is that Azure AI Vision's landmark detection returns the name of a recognized physical landmark, such as the Eiffel Tower or Big Ben, along with a confidence score. This is correct because the service uses a pre-trained computer vision model to analyze image content, matching visual features against a database of known global landmarks, and then outputs both the specific landmark identifier and a numeric score (typically between 0 and 1) that quantifies the model's certainty in the match. On the AI-900 exam, this tests your understanding of pre-built Azure AI Vision capabilities versus custom vision solutions, often appearing in scenario-based questions about photo tagging or travel applications. A common trap is confusing landmark detection with optical character recognition or object detection—remember that landmarks are specific, named places, not generic objects. For a quick memory tip, think "Landmark = Name + Score," as the service always pairs the location's identity with a reliability rating.
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 'Azure AI Vision's landmark detection' and what does it return?
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
Identifying well-known physical landmarks (Eiffel Tower, Big Ben) in photographs with a confidence score
Azure AI Vision's landmark detection is a pre-built computer vision capability that identifies well-known physical landmarks (e.g., Eiffel Tower, Big Ben) in images. It returns the landmark name along with a confidence score indicating the likelihood of the match, enabling applications like automated photo tagging or travel content enrichment.
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.
- ✗
Detecting important milestones in a project timeline using AI
Why it's wrong here
Project milestones are project management — landmark detection identifies physical world famous monuments and natural features.
- ✓
Identifying well-known physical landmarks (Eiffel Tower, Big Ben) in photographs with a confidence score
Why this is correct
Landmark detection names famous locations from photos — enabling automatic location tagging and travel content analysis.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Creating highlighted markers on maps showing user-defined points of interest
Why it's wrong here
Map markers are GIS tools — landmark detection identifies famous locations in existing photos using AI.
- ✗
Detecting major architectural features of any building regardless of whether it is famous
Why it's wrong here
Generic architectural analysis is part of image captioning — landmark detection specifically recognises globally famous named locations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing 'landmark detection' with generic object detection or architectural feature recognition, leading candidates to choose Option D, which incorrectly assumes any building can be identified.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Vision uses a deep convolutional neural network trained on millions of images of globally recognized landmarks, outputting a confidence score (0–1) for each detected landmark. The service supports over 9,000 landmarks worldwide and can return multiple landmarks per image if present. A real-world scenario is a travel app automatically tagging photos with the correct landmark name and confidence, allowing users to filter or search their gallery by location.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
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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: Identifying well-known physical landmarks (Eiffel Tower, Big Ben) in photographs with a confidence score — Azure AI Vision's landmark detection is a pre-built computer vision capability that identifies well-known physical landmarks (e.g., Eiffel Tower, Big Ben) in images. It returns the landmark name along with a confidence score indicating the likelihood of the match, enabling applications like automated photo tagging or travel content enrichment.
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
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.
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