- A
Automatically scheduling meetings with celebrities based on their availability
Why wrong: Meeting scheduling is calendar integration — celebrity recognition identifies famous people in images.
- B
Identifying well-known public figures in images and returning their names with confidence scores
Celebrity recognition uses a specialized domain model to identify famous public figures in images for media and content applications.
- C
Generating fictional celebrity lookalikes for entertainment applications
Why wrong: AI-generated lookalikes are generative AI — celebrity recognition identifies real, known public figures.
- D
Verifying celebrity identities for event access control
Why wrong: Access control uses face verification — celebrity recognition identifies public figures in images but isn't designed for security access control.
Quick Answer
The correct answer is that Azure AI Vision’s celebrity recognition feature identifies well-known public figures in images and returns their names with confidence scores. This capability relies on a pre-trained, domain-specific model that goes beyond general object detection, using a curated dataset of celebrity faces to match facial features against known public figures like actors, politicians, and athletes. On the AI-900 exam, this question tests your understanding of specialized vision services versus general facial recognition—a common trap is confusing celebrity recognition with the broader Face API, which identifies unknown individuals. Remember that celebrity recognition is a pre-built, narrow AI model, not a custom training task. A useful memory tip: think “famous faces, fixed model” to recall that it returns names and scores without requiring you to train the system.
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 capability does Azure AI Vision's 'celebrity recognition' feature provide?
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 public figures in images and returning their names with confidence scores
Azure AI Vision's celebrity recognition feature is a specialized domain-specific model that identifies well-known public figures (e.g., actors, politicians, athletes) within images. It returns the recognized celebrity's name along with a confidence score, enabling applications like media indexing or social media analysis. This capability is built on top of the general object detection and facial recognition models, but is pre-trained on a curated dataset of celebrity faces.
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.
- ✗
Automatically scheduling meetings with celebrities based on their availability
Why it's wrong here
Meeting scheduling is calendar integration — celebrity recognition identifies famous people in images.
- ✓
Identifying well-known public figures in images and returning their names with confidence scores
Why this is correct
Celebrity recognition uses a specialized domain model to identify famous public figures in images for media and content applications.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Generating fictional celebrity lookalikes for entertainment applications
Why it's wrong here
AI-generated lookalikes are generative AI — celebrity recognition identifies real, known public figures.
- ✗
Verifying celebrity identities for event access control
Why it's wrong here
Access control uses face verification — celebrity recognition identifies public figures in images but isn't designed for security access control.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse celebrity recognition (a pre-built, domain-specific model for identifying famous people) with general facial recognition or verification, which are separate capabilities in Azure AI Vision with different use cases and APIs.
Detailed technical explanation
How to think about this question
Under the hood, the celebrity recognition model uses a deep neural network trained on a large corpus of labeled celebrity images, leveraging facial landmarks and embeddings to match against a known gallery. The confidence score is derived from the cosine similarity between the detected face embedding and the closest celebrity embedding in the model's internal database. A real-world scenario is a news organization automatically tagging public figures in photo archives, where the model can handle variations in pose, lighting, and age but may fail for less famous or recently emerged individuals not in the training set.
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
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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 public figures in images and returning their names with confidence scores — Azure AI Vision's celebrity recognition feature is a specialized domain-specific model that identifies well-known public figures (e.g., actors, politicians, athletes) within images. It returns the recognized celebrity's name along with a confidence score, enabling applications like media indexing or social media analysis. This capability is built on top of the general object detection and facial recognition models, but is pre-trained on a curated dataset of celebrity faces.
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.
About these practice questions
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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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