- A
Azure AI Vision
Why wrong: Azure AI Vision provides general image analysis — Azure AI Face is the dedicated service for face detection and analysis.
- B
Azure AI Face
Azure AI Face detects faces in images and provides attributes like age estimate, emotion, and supports face verification.
- C
Azure AI Custom Vision
Why wrong: Custom Vision trains custom image classifiers — face detection and analysis is done by Azure AI Face.
- D
Azure AI Video Indexer
Why wrong: Video Indexer analyzes video content — Azure AI Face handles face detection in images.
Question 759 of 1,020
Which Azure AI Service Detects Faces and Returns Age Estimate and Emotion?
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.
Which Azure AI service detects and identifies human faces in images, including attributes like age estimate and emotion?
Quick Answer
The answer is Azure AI Face. This service is the correct choice because it is purpose-built for detecting and identifying human faces in images, using specialized models that go beyond general image analysis to extract detailed attributes like age estimates and emotions such as happiness or sadness. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your ability to distinguish between Azure AI services: while Azure AI Vision offers broader image analysis, Azure AI Face is the only one that returns face rectangles and optional attribute data for age and emotion. A common trap is confusing it with Azure AI Video Indexer, which analyzes video rather than static images. To remember, think of the service name literally—Face—and associate it with the specific attributes it returns: age and emotion are the two most frequently tested.
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
✓
Azure AI Face
Azure AI Face is the correct service because it is specifically designed to detect and identify human faces in images, and it can extract attributes such as age estimates, emotions (e.g., happiness, sadness), and facial landmarks. Unlike general-purpose image analysis, Azure AI Face uses specialized face detection models and returns face rectangles along with optional attribute data.
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 AI Vision
Why it's wrong here
Azure AI Vision provides general image analysis — Azure AI Face is the dedicated service for face detection and analysis.
- ✓
Azure AI Face
Why this is correct
Azure AI Face detects faces in images and provides attributes like age estimate, emotion, and supports face verification.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure AI Custom Vision
Why it's wrong here
Custom Vision trains custom image classifiers — face detection and analysis is done by Azure AI Face.
- ✗
Azure AI Video Indexer
Why it's wrong here
Video Indexer analyzes video content — Azure AI Face handles face detection in images.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Azure AI Vision's basic face detection (which only returns bounding boxes) with Azure AI Face's specialized attribute extraction, leading them to select Azure AI Vision when the question explicitly asks for age estimate and emotion attributes.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Face uses deep neural networks trained on large datasets of labeled faces to detect up to 64 faces per image and return attributes such as age (as a floating-point range), emotion (with confidence scores for eight categories), and head pose (pitch, roll, yaw). A subtle behavior is that the age attribute is an estimate and may vary by up to 5 years due to model training data biases, and emotion detection is based on facial expressions, not actual emotional state. In a real-world scenario, a retail application might use Azure AI Face to analyze customer demographics and reactions at a kiosk, but it must comply with responsible AI guidelines and cannot be used for sensitive identification without consent.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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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: Azure AI Face — Azure AI Face is the correct service because it is specifically designed to detect and identify human faces in images, and it can extract attributes such as age estimates, emotions (e.g., happiness, sadness), and facial landmarks. Unlike general-purpose image analysis, Azure AI Face uses specialized face detection models and returns face rectangles along with optional attribute data.
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
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