AI-900 Practice Question: Describe features of computer vision workloads on Azure
What is the Azure AI Face service's 'face verification' capability?
⚠ Common exam trap
Candidates often confuse 'face verification' (one-to-one matching) with 'face identification' (one-to-many matching), leading them to select option D, which describes identification against a large database.
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
✓
Comparing two facial images to determine if they belong to the same person
Azure AI Face service's 'face verification' capability is designed to compare two facial images and determine if they belong to the same person. It returns a confidence score and a boolean result indicating whether the faces match, based on a user-defined threshold. This is distinct from identification, which matches against a larger database.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Confirming that detected faces belong to humans and not artificial representations
Why it's wrong here
Determining whether detected faces are genuine human faces as opposed to photos, videos, or masks is liveness detection (anti-spoofing), a separate security layer. Liveness methods analyze texture, depth, motion, or infrared reflectance to detect a live subject. Face verification itself only compares two images for identity match; it does not assess whether either face is a real person rather than an artifact.
- ✓
Comparing two facial images to determine if they belong to the same person
Why this is correct
Face verification is the one-to-one (1:1) comparison of two facial images, typically a live capture and an enrolled reference, to determine whether they belong to the same person. The system extracts face embeddings and computes a similarity score; if this score exceeds a predetermined threshold, the identities are deemed a match. This operation is used for authentication, access control, and identity proofing.
- ✗
Verifying that facial recognition results meet accuracy requirements
Why it's wrong here
Verifying that facial recognition results meet accuracy requirements describes system evaluation or benchmark testing, not the face verification operation. Accuracy is measured through metrics such as false acceptance rate (FAR) and false rejection rate (FRR) on a test dataset, allowing you to tune the matching threshold. Face verification, by contrast, is the actual runtime comparison of two faces to determine whether they represent the same individual.
- ✗
Confirming the identity of a known person against a database of millions
Why it's wrong here
Matching a known person against a database of millions is face identification (1:N search), not verification. In identification, a query face is compared to every enrolled identity and the system returns the closest match or a list of candidates. Verification instead follows a one-to-one (1:1) flow, comparing one query face with one specific reference face to confirm whether they are the same person.
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
Key term
Confidence score
A confidence score is a number (often between 0 and 1 or 0 and 100%) that tells you how likely it is that an AI model's prediction or answer is correct.
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