The correct answer is age estimate, emotion, head pose, and other facial attributes. Azure AI Face service goes far beyond simply returning a bounding box for each detected face; it analyzes the facial geometry and texture to extract rich attribute data, including estimated age, emotional state (such as happiness, sadness, or surprise), head pose angles (pitch, yaw, and roll), facial hair, glasses, and even skin tone or makeup. On the AI-900 exam, this question tests your understanding of the service’s full capability versus its basic detection function—a common trap is confusing the Face service with the simpler Computer Vision face detection, which only provides coordinates. Remember that Azure AI Face is designed for detailed attribute extraction, not just location. A helpful memory tip: think of the acronym “AHEaP” for Age, Head pose, Emotion, and additional attributes like glasses and facial hair—these are the key extras beyond the box.
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 information does Azure AI Face service provide about detected faces beyond just their location?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Age estimate, emotion, head pose, and other facial attributes
Azure AI Face service can extract a wide range of facial attributes beyond just the bounding box coordinates. These include age estimate, emotion (e.g., happiness, sadness, surprise), head pose (pitch, yaw, roll), facial hair, glasses, and more, making option B correct.
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.
✗
Only the coordinates of the face bounding box
Why it's wrong here
Face detection returns bounding boxes, but the Face service provides rich attributes like age, emotion, and head pose.
✓
Age estimate, emotion, head pose, and other facial attributes
Why this is correct
Azure AI Face returns age estimates, detected emotion, glasses type, head pose, and other attributes alongside the face location.
Related concept
Read the scenario before looking for a memorised answer.
✗
The person's name and identity from a public database
Why it's wrong here
Face service doesn't search public databases — face identification compares against an enrolled private group of known individuals.
✗
Only whether the face belongs to a human or not
Why it's wrong here
Face detection assumes human faces — the service provides detailed attribute analysis beyond just detecting presence.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume the Face service only provides basic location data (bounding box) or mistakenly think it can look up identities from public databases like social media, when in fact it requires custom enrollment for identification.
Detailed technical explanation
How to think about this question
Under the hood, the Face service uses deep neural networks trained on large datasets to predict attributes like emotion via a multi-class classifier (e.g., eight emotion categories) and head pose via 3D rotation angles. A subtle behavior is that emotion detection is not always accurate for subtle expressions or non-frontal faces, and age estimation is a range (e.g., 20–30) rather than a precise number. In a real-world scenario, a retail app might use head pose to detect customer attention and emotion to gauge satisfaction, but must handle false positives for non-human faces (e.g., mannequins).
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: Age estimate, emotion, head pose, and other facial attributes — Azure AI Face service can extract a wide range of facial attributes beyond just the bounding box coordinates. These include age estimate, emotion (e.g., happiness, sadness, surprise), head pose (pitch, yaw, roll), facial hair, glasses, and more, making option B correct.
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.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What is 'face attribute analysis' in Azure AI Face service?
medium
A.Identifying the named person in a photograph using a face database
✓ B.Estimating age, emotion, head pose, and appearance attributes from detected faces
C.Verifying whether a submitted selfie matches a government-issued ID document
D.Detecting whether a face has been digitally manipulated or deepfaked
Why B: Face attribute analysis in Azure AI Face service extracts a set of facial attributes from detected faces, including estimated age, emotion (e.g., happiness, sadness, anger), head pose (pitch, yaw, roll), and appearance traits like facial hair, glasses, and makeup. This is distinct from identification or verification tasks because it does not match faces against a database or compare two images; it simply returns metadata about the face itself.
Last reviewed: Jun 11, 2026
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