- A
Confirming that detected faces belong to humans and not artificial representations
Why wrong: Distinguishing real faces from artificial ones is liveness detection — face verification compares two faces to determine if they're the same person.
- B
Comparing two facial images to determine if they belong to the same person
Face verification (1:1 comparison) returns a confidence score for whether two faces are the same individual — used in identity verification.
- C
Verifying that facial recognition results meet accuracy requirements
Why wrong: Accuracy validation is evaluation — face verification compares specific face pairs to determine identity matching.
- D
Confirming the identity of a known person against a database of millions
Why wrong: Matching against a large database is face identification (1:N) — verification is a 1:1 comparison of two specific faces.
Quick Answer
The answer is comparing two facial images to determine if they belong to the same person. This is the core of the Azure AI Face service's face verification capability, which analyzes facial features from two provided images and returns a confidence score along with a boolean match result based on a user-defined threshold. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your ability to distinguish verification from identification—verification is a one-to-one comparison, while identification matches against a larger enrolled database. A common trap is confusing these two; remember that verification asks "Is this you?" for a single pair, whereas identification asks "Who is this?" across many faces. For a quick memory tip, think of verification as a "pair check" (two faces, one verdict), and identification as a "group search" (one face, many candidates).
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 the Azure AI Face service's 'face verification' capability?
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.
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.
- ✗
Confirming that detected faces belong to humans and not artificial representations
Why it's wrong here
Distinguishing real faces from artificial ones is liveness detection — face verification compares two faces to determine if they're the same person.
- ✓
Comparing two facial images to determine if they belong to the same person
Why this is correct
Face verification (1:1 comparison) returns a confidence score for whether two faces are the same individual — used in identity verification.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Verifying that facial recognition results meet accuracy requirements
Why it's wrong here
Accuracy validation is evaluation — face verification compares specific face pairs to determine identity matching.
- ✗
Confirming the identity of a known person against a database of millions
Why it's wrong here
Matching against a large database is face identification (1:N) — verification is a 1:1 comparison of two specific faces.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Under the hood, face verification uses a deep neural network to extract a unique feature vector (face template) from each image, then computes a similarity score between the two vectors using cosine distance. The service returns a confidence score between 0 and 1, and the user sets a threshold (e.g., 0.5) to decide a match. This is critical in scenarios like airport security or banking, where a one-to-one check against a stored ID photo is needed, not a search across many faces.
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
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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: 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.
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.
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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.
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