The answer is the Azure Face API, which is the correct choice because it is purpose-built for identity verification by comparing photos, such as matching a live customer photo against a government-issued ID. Technically, the Face API’s ‘Verify’ operation analyzes facial features from two images and returns a confidence score indicating whether they belong to the same person, enabling reliable identity verification. On the AI-900 exam, this scenario tests your understanding of which Azure Computer Vision service handles face comparison versus general image analysis or OCR; a common trap is confusing the Face API with the Computer Vision service, which lacks dedicated face verification capabilities. To remember, think of the Face API as the “ID checker” that specifically compares faces, while Computer Vision is the broader “image describer.”
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.
A retail company wants to build a system that can verify the identity of customers by comparing their live photo with an uploaded government-issued ID photo. Which Azure Computer Vision service should they use to perform the face comparison?
Why wrong: Image Analysis can describe images and detect objects, but it does not provide face verification, which requires comparing facial features between two images.
B
Azure Face API
Face API offers face verification, which checks if a live photo matches a reference photo (e.g., the ID photo) by comparing facial features.
C
Azure Custom Vision
Why wrong: Custom Vision is for training custom models to classify or detect objects, not for face verification out-of-the-box.
D
Azure Form Recognizer
Why wrong: Form Recognizer extracts text and tables from documents; it does not perform face analysis or comparison.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Face API
The Azure Face API is specifically designed for face detection, verification, and comparison tasks. It can compare a live photo against a reference photo (such as a government-issued ID) using its 'Verify' operation, which returns a confidence score indicating whether the two faces belong to the same person. This makes it the correct choice for identity verification scenarios.
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.
✗
Azure Computer Vision - Image Analysis
Why it's wrong here
Image Analysis can describe images and detect objects, but it does not provide face verification, which requires comparing facial features between two images.
✓
Azure Face API
Why this is correct
Face API offers face verification, which checks if a live photo matches a reference photo (e.g., the ID photo) by comparing facial features.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure Custom Vision
Why it's wrong here
Custom Vision is for training custom models to classify or detect objects, not for face verification out-of-the-box.
✗
Azure Form Recognizer
Why it's wrong here
Form Recognizer extracts text and tables from documents; it does not perform face analysis or comparison.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse the general-purpose Azure Computer Vision - Image Analysis service with the specialized Face API, assuming that any computer vision service can perform face comparison, when in fact only the Face API provides dedicated face verification functionality.
Detailed technical explanation
How to think about this question
The Face API's 'Verify' operation uses deep neural networks to extract facial features (such as landmarks and embeddings) and computes a similarity score between two face images. It also supports liveness detection to prevent spoofing attacks, which is critical in identity verification scenarios like comparing a live selfie to an ID photo. The API returns a confidence score between 0.0 and 1.0, with a recommended threshold of 0.5 for most applications.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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: Azure Face API — The Azure Face API is specifically designed for face detection, verification, and comparison tasks. It can compare a live photo against a reference photo (such as a government-issued ID) using its 'Verify' operation, which returns a confidence score indicating whether the two faces belong to the same person. This makes it the correct choice for identity verification scenarios.
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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Question Discussion
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