- A
Detecting whether a celebrity face in a photograph is still alive or deceased
Why wrong: This is a morbid misreading — liveness detection determines if a face is a live person or a spoofing artefact for security.
- B
Verifying that a face presented to a camera is a real live person, not a photo or video replay
Liveness detection prevents face spoofing attacks — distinguishing a live face from a photograph or video used for fraudulent authentication.
- C
Detecting human faces in real-time video streaming from security cameras
Why wrong: Real-time face detection in video is a broader capability — liveness specifically verifies the face is live, not a spoofed artefact.
- D
Monitoring whether a face recognition model remains accurate after deployment
Why wrong: Model monitoring is MLOps — liveness detection is a security anti-spoofing capability for identity verification.
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 'liveness detection' in Azure AI Face service?
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
Verifying that a face presented to a camera is a real live person, not a photo or video replay
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.
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 celebrity face in a photograph is still alive or deceased
Why it's wrong here
This is a morbid misreading — liveness detection determines if a face is a live person or a spoofing artefact for security.
- ✓
Verifying that a face presented to a camera is a real live person, not a photo or video replay
Why this is correct
Liveness detection prevents face spoofing attacks — distinguishing a live face from a photograph or video used for fraudulent authentication.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Detecting human faces in real-time video streaming from security cameras
Why it's wrong here
Real-time face detection in video is a broader capability — liveness specifically verifies the face is live, not a spoofed artefact.
- ✗
Monitoring whether a face recognition model remains accurate after deployment
Why it's wrong here
Model monitoring is MLOps — liveness detection is a security anti-spoofing capability for identity verification.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse liveness detection with general face detection or recognition, assuming any real-time face processing qualifies, when in fact liveness detection specifically addresses anti-spoofing and presentation attack detection.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Face liveness detection uses a combination of passive and active checks: passive methods analyze micro-movements (e.g., involuntary eye blinks) and texture anomalies (e.g., moiré patterns from screens), while active methods may prompt the user to perform a specific action like turning their head. In real-world scenarios, this is critical for high-security applications like banking or border control, where a spoofed face could bypass traditional facial recognition. The service leverages deep learning models trained on diverse spoofing attacks to achieve high accuracy against print, video, and mask 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Describe features of computer vision workloads on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe features of computer vision workloads on Azure practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Verifying that a face presented to a camera is a real live person, not a photo or video replay — 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.
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 →
Keep practising
More AI-900 practice questions
- A company deploys an AI system to screen job applications. The system is a complex neural network that learns patterns f…
- What is 'model versioning' and why is it essential in MLOps?
- What is 'AI transparency' in Microsoft's Responsible AI principles?
- A company uses Azure OpenAI Service to generate marketing copy. They notice that sometimes the generated text contains r…
- A data scientist is training a regression model to predict house prices using features like square footage, number of be…
- A company uses Azure OpenAI Service to generate marketing copy. They want to ensure that the generated text does not con…
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.