BenchMark Quizzes by MDFT Pro

Integrate Active Learning Into QnA Maker Knowledge Base

MDFT Pro, a well-known training agency, operates a student support chatbot that uses QnA Maker to answer frequently asked questions about courses, enrollment, and certification requirements. Mark, the Knowledge Base Manager, has enabled active learning for their QnA Maker application to improve the chatbot’s ability to handle student inquiries.

The system has been collecting questions from real student interactions where the chatbot couldn’t provide satisfactory answers. Now Mark needs to integrate these user-generated insights back into the knowledge base to enhance the chatbot’s performance and ensure it can better serve future student questions.

Which four actions should Mark perform in sequence to integrate user input into the QnA Maker model through active learning?

Choose all correct answers from the options below.

Explanations for each answer:

Select Show active learning suggestions for the knowledge base is correct. This step displays the active learning suggestions that have been collected from user interactions, showing questions that users asked but didn't receive satisfactory answers to.
Approve and reject suggestions is correct. Review each suggestion and decide whether to approve it as a new QnA pair or reject it if it's not suitable for the knowledge base.
Save and train the knowledge base is correct. After approving suggestions, the knowledge base must be saved and retrained to incorporate the new question-answer pairs into the model.
Publish the knowledge base is correct. Publishing makes the updated knowledge base with new QnA pairs available to the live chatbot for student interactions.
Add a task to the Azure resource is incorrect. Adding tasks to Azure resources is not part of the QnA Maker active learning integration process.

Learn more about QnA Maker active learning:

QnA Maker Active Learning

Exam Preparation Quiz

Integrate Active Learning Into QnA Maker Knowledge Base