AI-900 Practice Question: Describe features of computer vision workloads on Azure
Which Azure AI service detects and identifies human faces in images, including attributes like age estimate and emotion?
⚠ Common exam trap
Many 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.
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.
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 performs general image analysis such as object detection, optical character recognition, and tagging, and while it can locate faces in an image, it does not offer the specialized face-centric capabilities needed here. The question specifically asks for face detection, age estimation, emotion recognition, and face verification, which are the domain of Azure AI Face. Therefore selecting Azure AI Vision would be incorrect because it lacks the depth of facial attribute analysis and identity verification.
- ✓
Azure AI Face
Why this is correct
Azure AI Face is the correct choice because it is a dedicated Azure Cognitive Service built specifically for face detection, face verification, and facial attribute analysis. It detects one or more human faces in an image and returns face rectangles, along with attributes such as estimated age, emotion, and head pose. It also supports face verification by comparing faces against a gallery, making it the appropriate service for requirements like age estimate, emotion, and face verification.
- ✗
Azure AI Custom Vision
Why it's wrong here
Azure AI Custom Vision enables you to train a bespoke image classification or object detection model using your own labeled data, making it suitable for specialized visual domains, but it does not come pre-built with face detection, age estimation, emotion recognition, or face verification. The prerequisite for using Custom Vision would be to build and train a custom model from scratch, which is unnecessary and less accurate than the out-of-the-box Azure AI Face service. Therefore Custom Vision is incorrect for this scenario because the required facial recognition capabilities are already provided by Azure AI Face.
- ✗
Azure AI Video Indexer
Why it's wrong here
Azure AI Video Indexer is designed for extracting insights from video content, such as scenes, keywords, and faces detected across frames in a video, but it is not the recommended service for analyzing a single static image. Face detection and facial attributes like age, emotion, and verification on still images are handled by Azure AI Face, which is optimized for that specific task. Thus choosing Video Indexer is wrong because it targets a different media type and pipeline—video indexing rather than image-level face analysis.
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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
Face detection
Face detection is an AI service that identifies and locates human faces in images or video, distinguishing them from other objects or backgrounds.
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Written by Johnson Ajibi, MSc IT Security
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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.