This AI-102 practice question tests your understanding of implement computer vision solutions. 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.
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?
Set a high confidence threshold for face matching.
Why wrong: Thresholds reduce false positives but do not prevent spoofing.
Face identification with a large person group.
Why wrong: Identification does not detect liveness.
Enable liveness detection using session-based verification.
Liveness detection checks for spoofing attacks.
Face detection with attributes such as age and emotion.
Why wrong: Attributes do not verify liveness.
Answer choices
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.
Option C is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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
Thresholds reduce false positives but do not prevent spoofing.
Face identification with a large person group.
Why it's wrong here
Identification does not detect liveness.
Enable liveness detection using session-based verification.
Why this is correct
Liveness detection checks for spoofing attacks.
Related concept
Read the scenario before looking for a memorised answer.
Face detection with attributes such as age and emotion.
Why it's wrong here
Attributes do not verify liveness.
Common exam traps
The trap here is that candidates may 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.
Detailed technical explanation
Session-based liveness detection in Azure Face API uses a challenge-response mechanism, often requiring the user to perform a specific action (e.g., blink or turn head) during a short session. The API analyzes the sequence of frames to detect natural motion and depth inconsistencies, which are absent in static images or replayed videos. This approach is critical in high-security scenarios like building access, where even high-resolution photos can bypass simple confidence thresholds.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Implement computer vision solutions — This question tests Implement computer vision solutions — Read the scenario before looking for a memorised answer..
The correct answer is: Enable liveness detection using session-based verification. — Option C is correct because 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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