Explanation:

These answers are derived from the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure." The Azure Face service, part of Azure Cognitive Services, provides advanced facial recognition capabilities including detection, verification, identification, grouping, and similarity analysis.
Let's analyze each statement:
* "The Face service can be used to group all the employees who have similar facial characteristics." # YesThe Face service supports a grouping function that automatically organizes a collection of unknown faces into groups based on visual similarity. It doesn't require labeled data; instead, it identifies clusters of similar-looking faces. This is particularly useful when building or validating datasets of people.
* "The Face service will be more accurate if you provide more sample photos of each employee from different angles." # YesAccording to Microsoft documentation, model accuracy improves when you provide multiple high-quality images of each person under different conditions-such as varying lighting, poses, and angles. This diversity allows the service to better learn unique facial characteristics and improves recognition reliability, especially for identification and verification tasks.
* "If an employee is wearing sunglasses, the Face service will always fail to recognize the employee." # NoThis is incorrect. While occlusions (like sunglasses or hats) can reduce accuracy, the service may still recognize the person depending on how much of the face remains visible. Microsoft Learn explicitly notes that partial occlusion affects recognition confidence but does not guarantee failure.
In conclusion, the Face service can group similar faces (Yes), become more accurate with diverse samples (Yes), and still recognize partially covered faces though with lower confidence (No). These principles align directly with the Face API's core functions and AI-900 learning objectives regarding computer vision and responsible AI-based facial recognition.