AI-900 Practice Question: Describe features of computer vision workloads on Azure

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

  Liveness detection is a real-time anti-spoofing mechanism, not a forensic mortality classifier. It checks for physiological signs of a live person, such as blinking, skin texture, or 3D depth, during a biometric capture. A photograph of a living celebrity would be rejected as a spoof, and a photo of a deceased person would also be flagged—it cannot infer whether the person is alive today.

• ✓

  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 verifies that a face being presented to a camera is a physically present human, not a printed photo, phone screen, or video replay. It uses active challenges (blinking, head turning) and passive cues (depth, texture, illumination, micro-movements) to defeat presentation attacks. Azure Face API's liveness check returns 'live' or 'spoof,' making it a critical security layer for facial authentication systems.

• ✗

  Detecting human faces in real-time video streaming from security cameras

  Why it's wrong here

  Real-time face detection in security-camera video simply localizes and tracks faces frame by frame using object-detection models; it does not judge whether the face belongs to a person physically in front of the camera. Such a system would still report a face displayed on a phone or printed on a photo, whereas liveness detection explicitly distinguishes these spoofing artifacts. Liveness also typically requires an interactive enrollment or verification session rather than passive surveillance of a crowd.

• ✗

  Monitoring whether a face recognition model remains accurate after deployment

  Why it's wrong here

  Monitoring post-deployment model accuracy is an MLOps practice that tracks metrics such as precision, recall, data drift, or fairness to detect when a face recognition model's performance degrades over time. Liveness detection, by contrast, is a runtime security function evaluated at every authentication attempt to block spoofing. These are entirely different concerns: model monitoring keeps the system healthy, while liveness protects a single verification event from presentation attacks.

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