Exam AI-100 topic 1 question 42 discussion

HOTSPOT -
You plan to create an intelligent bot to handle internal user chats to the help desk of your company. The bot has the following requirements:
✑ Must be able to interpret what a user means.
✑ Must be able to perform multiple tasks for a user.
Must be able to answer questions from an existing knowledge base.

You need to recommend which solutions meet the requirements.
Which solution should you recommend for each requirement? To answer, drag the appropriate solutions to the correct requirements. Each solution may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Hot Area:

Suggested Answer:
Box 1: The Language Understanding (LUIS) service
Language Understanding (LUIS) is a cloud-based API service that applies custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.

Box 2: Text Analytics API -
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.

Box 3: The QnA Maker service -
QnA Maker is a cloud-based Natural Language Processing (NLP) service that easily creates a natural conversational layer over your data. It can be used to find the most appropriate answer for any given natural language input, from your custom knowledge base (KB) of information.
Incorrect Answers:
Dispatch tool library:
If a bot uses multiple LUIS models and QnA Maker knowledge bases (knowledge bases), you can use Dispatch tool to determine which LUIS model or QnA Maker knowledge base best matches the user input. The dispatch tool does this by creating a single LUIS app to route user input to the correct model.
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-dispatch https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/overview/overview