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Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You have a chatbot that uses question answering in Azure Cognitive Service for Language Users report that the responses of the chatbot lark formality when answering spurious questions You need to ensure that the chatbot provides formal responses to spurious questions. Solution: From Language Studio, you change the chitchat source to qna_chitchit_friindly.tsv. and then retrain and republish the model. Does this meet the goal?
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Question 61 - AI-102 discussion

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You are training a Language Understanding model for a user support system.

You create the first intent named GetContactDetails and add 200 examples.

You need to decrease the likelihood of a false positive.

What should you do?

A.
Enable active learning.
Answers
A.
Enable active learning.
B.
Add a machine learned entity.
Answers
B.
Add a machine learned entity.
C.
Add additional examples to the GetContactDetails intent.
Answers
C.
Add additional examples to the GetContactDetails intent.
D.
Add examples to the None intent.
Answers
D.
Add examples to the None intent.
Suggested answer: A

Explanation:

Active learning is a technique of machine learning in which the machine learned model is used to identify informative new examples to label. In LUIS, active learning refers to adding utterances from the endpoint traffic whose current predictions are unclear to improve your model.

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-glossary

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asked 26/09/2024
PATRICK KOUOBOU
29 questions
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