You need to develop a web-based AI solution for a customer support system. Users must be able to interact with a web app that will guide them to the best resource or answer.
Which service should you use?
Click on the arrows to vote for the correct answer
A. B. C. D.B
QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semi-structured content, including FAQs, manuals, and documents. Answer users' questions with the best answers from the QnAs in your knowledge base"automatically. Your knowledge base gets smarter, too, as it continually learns from user behavior.
Incorrect Answers:
A: Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.
D: Azure Cognitive Services Face Detection API: At a minimum, each detected face corresponds to a faceRectangle field in the response. This set of pixel coordinates for the left, top, width, and height mark the located face. Using these coordinates, you can get the location of the face and its size. In the API response, faces are listed in size order from largest to smallest.
https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/The service that would best fit the requirement to develop a web-based AI solution for a customer support system where users can interact with a web app to find the best resource or answer is the QnA Maker.
QnA Maker is a cloud-based cognitive service in Azure that enables developers to create a conversational question-and-answer layer over their data. It allows users to quickly create and publish a knowledge base from frequently asked questions (FAQs), support documents, and product manuals. This service uses natural language processing (NLP) to match user queries to the most relevant answer from the knowledge base.
Using QnA Maker, developers can create a knowledge base by simply pointing to an existing FAQ page or document, and then train it to understand natural language queries. Once the knowledge base is created and trained, developers can integrate it into their web application using various channels, including a REST API, a Bot Framework SDK, and a JavaScript widget.
In the scenario presented, users can interact with a web app that will guide them to the best resource or answer. This is exactly what QnA Maker does best. By integrating QnA Maker into the web app, users can ask questions in natural language, and the system will use NLP to find the best matching answer from the knowledge base. The QnA Maker service is customizable, so developers can modify the answers and add more context if needed to improve accuracy.
Therefore, the correct answer to the question is option B, QnA Maker.