AI-102 Implement computer vision solutions Practice Question
A company uses Azure Face API to verify employee identities for building access. They need to ensure that only live faces are used, not photos or videos. Which feature should they enable?
⚠ Common exam trap
Test-takers frequently confuse confidence thresholds or face attributes with liveness detection, not realizing that only session-based verification actively checks for spoofing through motion and depth analysis.
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
✓
Enable liveness detection using session-based verification.
Azure Face API's liveness detection with session-based verification is specifically designed to prevent spoofing attacks using photos, videos, or masks. It analyzes subtle cues such as micro-movements, texture, and depth to confirm the presence of a live person, ensuring that only live faces are accepted for identity verification.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Set a high confidence threshold for face matching.
Why it's wrong here
A confidence threshold governs how closely a detected face must match an enrolled person; it does nothing to distinguish a live person from a photograph presented to the camera. Liveness detection is the feature that rejects spoofing attempts, and it is what this scenario requires.
- ✗
Face identification with a large person group.
Why it's wrong here
Identification against a large person group matches a detected face to an enrolled individual; it does not verify that the face belongs to a live person rather than a photograph or video. Liveness detection is the feature that rejects spoofing attempts, which is what building access requires.
- ✓
Enable liveness detection using session-based verification.
Why this is correct
Session-based liveness detection challenges the subject to perform a randomised action, then analyses the response to confirm a live person rather than a static photo or replayed video. This satisfies the requirement to reject spoofing attempts during identity verification.
- ✗
Face detection with attributes such as age and emotion.
Why it's wrong here
Detection attributes such as age and emotion merely return estimated characteristics of a detected face; they cannot determine whether the face is physically present or a photo or video replay. Liveness detection is the feature that defeats such spoofing, which is the stated requirement here.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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