Learn how to use active learning to enhance your QnA Maker chatbot’s response relevance based on user feedback. Explore the Azure AI Fundamentals exam question and find the correct answer.
Table of Contents
Question
You are setting up a webchat bot, that provides responses from a QnA Maker knowledge base. You have been provided with following use case:
Ensure that the bot uses user feedback to improve the relevancy of the responses over time.
What should you use?
A. Sentiment analysis
B. Active learning
C. Business logic
D. Key phrase extraction
Answer
To ensure that a QnA Maker-based webchat bot uses user feedback to improve the relevance of its responses over time, you should use active learning (Option B).
B. Active learning
Explanation
Active learning is a feature in QnA Maker that allows the bot to learn from user interactions and improve its performance. When active learning is enabled, the bot presents users with multiple answer options if it’s not highly confident in its initial response. Based on the user’s selection, QnA Maker learns which answers are most relevant for specific questions. This feedback is then used to refine the knowledge base and improve the bot’s accuracy in future interactions.
The other options mentioned are not directly related to improving response relevance based on user feedback:
- Sentiment analysis (Option A) is used to determine the emotional tone of user input, but doesn’t directly improve response relevance.
- Business logic (Option C) refers to the custom rules and workflows implemented in a bot, but doesn’t necessarily involve user feedback for improvement.
- Key phrase extraction (Option D) is a technique used to identify important phrases in text, but it doesn’t help improve response relevance based on user interactions.
In summary, to ensure that a QnA Maker-based webchat bot leverages user feedback to enhance its response relevance over time, you should implement active learning (Option B).
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