- A
Detecting whether a person is alive based on their vital signs
Why wrong: Vital sign detection requires medical sensors — liveness detection determines if a face is from a live person vs. a photo/mask spoofing attempt.
- B
Determining whether a face is from a live person or a spoofing attempt (photo/video/mask)
Liveness detection prevents authentication spoofing attacks by verifying the face is from a real, live person present at the camera.
- C
Counting how many people are in a live video stream
Why wrong: People counting uses spatial analysis — liveness detection is specifically for anti-spoofing in facial authentication.
- D
Monitoring whether a person remains present during a video call
Why wrong: Presence detection in video calls is a meeting tool — liveness detection is specifically for preventing authentication spoofing.
Quick Answer
The correct answer is that Azure AI Face service’s liveness detection is used to determine whether a face is from a live person or a spoofing attempt, such as a photo, video replay, or 3D mask. This feature works by analyzing subtle cues like micro-movements, skin texture, and depth to verify the presence of a living human, effectively blocking presentation attacks that try to trick facial recognition systems. On the AI-900 exam, this concept tests your understanding of how Azure AI addresses security in identity verification scenarios—expect a scenario where a system needs to prevent unauthorized access using a printed photo. A common trap is confusing liveness detection with simple face detection or verification; remember that liveness adds a “proof of life” layer. For a memory tip, think of the three spoofing types: photo, video, mask—and recall that liveness catches all three by checking for “life signs” like blinking or skin texture.
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 the Azure AI Face service's 'liveness detection' feature used for?
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
Determining whether a face is from a live person or a spoofing attempt (photo/video/mask)
Option B is correct because Azure AI Face's liveness detection is specifically designed to differentiate between a real, live human face and spoofing artifacts such as printed photos, video replays, or 3D masks. It analyzes subtle cues like micro-movements, texture, and depth to verify the presence of a living person, preventing unauthorized access in facial recognition systems.
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 whether a person is alive based on their vital signs
Why it's wrong here
Vital sign detection requires medical sensors — liveness detection determines if a face is from a live person vs. a photo/mask spoofing attempt.
- ✓
Determining whether a face is from a live person or a spoofing attempt (photo/video/mask)
Why this is correct
Liveness detection prevents authentication spoofing attacks by verifying the face is from a real, live person present at the camera.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Counting how many people are in a live video stream
Why it's wrong here
People counting uses spatial analysis — liveness detection is specifically for anti-spoofing in facial authentication.
- ✗
Monitoring whether a person remains present during a video call
Why it's wrong here
Presence detection in video calls is a meeting tool — liveness detection is specifically for preventing authentication spoofing.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse liveness detection with general presence detection or vital sign monitoring, leading them to choose options A or D, which describe unrelated features from other Azure services.
Detailed technical explanation
How to think about this question
Under the hood, liveness detection uses a combination of passive (e.g., analyzing texture and reflection from a single image) and active (e.g., requesting the user to blink or turn their head) methods to detect spoofing. In real-world scenarios, this is critical for high-security environments like banking or border control, where a printed photo or deepfake video could otherwise bypass a standard face verification system. The service leverages deep learning models trained on large datasets of live and spoofed faces to achieve high accuracy against presentation attacks.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Got this wrong? Here's your next step.
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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: Determining whether a face is from a live person or a spoofing attempt (photo/video/mask) — Option B is correct because Azure AI Face's liveness detection is specifically designed to differentiate between a real, live human face and spoofing artifacts such as printed photos, video replays, or 3D masks. It analyzes subtle cues like micro-movements, texture, and depth to verify the presence of a living person, preventing unauthorized access in facial recognition systems.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What is 'liveness detection' in Azure AI Face service?
easy- A.Detecting whether a celebrity face in a photograph is still alive or deceased
- ✓ B.Verifying that a face presented to a camera is a real live person, not a photo or video replay
- C.Detecting human faces in real-time video streaming from security cameras
- D.Monitoring whether a face recognition model remains accurate after deployment
Why B: Liveness detection in Azure AI Face service is a security feature that distinguishes between a real, live person and a spoofing attempt such as a printed photo, video replay, or a 3D mask. It analyzes subtle cues like eye blinking, skin texture, and depth to ensure the face presented to the camera is physically present and alive. This prevents unauthorized access in identity verification scenarios.
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Last reviewed: Jun 11, 2026
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