Learn which combination of Azure Cognitive Services like Language Understanding, QnA Maker, Text Analytics and others are required to create a chatbot with sentiment analysis and multilingual capabilities. Prepare for the AI-102 certification exam.
Table of Contents
Question
You need to build a chatbot that meets the following requirements:
- Supports chit-chat, knowledge base, and multilingual models
- Performs sentiment analysis on user messages
- Selects the best language model automatically
What should you integrate into the chatbot?
A. QnA Maker, Language Understanding, and Dispatch
B. Translator, Speech, and Dispatch
C. Language Understanding, Text Analytics, and QnA Maker
D. Text Analytics, Translator, and Dispatch
Answer
C. Language Understanding, Text Analytics, and QnA Maker
Explanation
Language Understanding: An AI service that allows users to interact with your applications, bots, and IoT devices by using natural language.
QnA Maker is a cloud-based Natural Language Processing (NLP) service that allows you to create a natural conversational layer over your data. It is used to find the most appropriate answer for any input from your custom knowledge base (KB) of information.
Text Analytics: Mine insights in unstructured text using natural language processing (NLP)ג€”no machine learning expertise required. Gain a deeper understanding of customer opinions with sentiment analysis. The Language Detection feature of the Azure Text Analytics REST API evaluates text input
Incorrect Answers:
A, B, D: Dispatch uses sample utterances for each of your bot’s different tasks (LUIS, QnA Maker, or custom), and builds a model that can be used to properly route your user’s request to the right task, even across multiple bots.
To build a chatbot that supports chit-chat, accesses a knowledge base, handles multiple languages, and performs sentiment analysis, you should integrate Language Understanding (LUIS), QnA Maker, and Text Analytics (option C).
Here’s why this combination of Azure Cognitive Services is the best choice:
- Language Understanding (LUIS) allows you to build natural language understanding into your chatbot. LUIS can interpret user queries, extract intents and entities, and help select the appropriate response. This enables more intelligent, conversational interactions beyond simple keyword matching.
- QnA Maker is ideal for creating a knowledge base of question-answer pairs that the chatbot can draw upon. You can ingest FAQ pages, product manuals, and other semi-structured content to quickly build up the chatbot’s knowledge. QnA Maker uses natural language processing to find the most relevant answer to a user’s question.
- Text Analytics provides sentiment analysis capabilities to determine whether a user’s message is positive, negative or neutral in tone. This insight can help the chatbot provide a more empathetic response and shape the direction of the conversation appropriately. Text Analytics also offers language detection, which can automatically identify the language of a user’s message.
The other options are incorrect because:
- Dispatch (in A and B) is used to route user queries to the appropriate LUIS app or QnA Maker knowledge base, but it does not itself provide chit-chat, knowledge base or language understanding capabilities.
- Translator (in B and D) is used for translating text between languages, but translating the chatbot’s responses is different than being able to handle multiple input languages and select the best model automatically. LUIS and language detection in Text Analytics are better suited for this.
- Speech (in B) is used for converting speech to text and vice versa, but it is not a core component needed for the outlined chatbot requirements.
Therefore, the best combination of Azure Cognitive Services to fulfill all the chatbot requirements is Language Understanding (LUIS), QnA Maker, and Text Analytics (option C).
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