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Free AI-102 Exam Braindumps (page: 34)

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PDF with 329 Questions

DRAG DROP (Drag and Drop is not supported)
You are building a retail chatbot that will use a QnA Maker service.
You upload an internal support document to train the model. The document contains the following question: "What is your warranty period?"
Users report that the chatbot returns the default QnA Maker answer when they ask the following question: "How long is the warranty coverage?"
The chatbot returns the correct answer when the users ask the following question: 'What is your warranty period?"
Both questions should return the same answer.
You need to increase the accuracy of the chatbot responses.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Select and Place:

  1. See Explanation section for answer.

Answer(s): A

Explanation:


Step 1: Add alternative phrasing to the question and answer (QnA) pair.
Add alternate questions to an existing QnA pair to improve the likelihood of a match to a user query.
Step 2: Retrain the model.
Periodically select Save and train after making edits to avoid losing changes.
Step 3: Republish the model
Note: A knowledge base consists of question and answer (QnA) pairs. Each pair has one answer and a pair contains all the information associated with that answer.


Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/edit-knowledge-base



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 build a language model by using a Language Understanding service. The language model is used to search for information on a contact list by using an intent named FindContact.
A conversational expert provides you with the following list of phrases to use for training.
-Find contacts in London.
-Who do I know in Seattle?
-Search for contacts in Ukraine.
You need to implement the phrase list in Language Understanding.
Solution: You create a new intent for location.
Does this meet the goal?

  1. Yes
  2. No

Answer(s): B

Explanation:

An intent represents a task or action the user wants to perform. It is a purpose or goal expressed in a user's utterance.
Define a set of intents that corresponds to actions users want to take in your application.


Reference:

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



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 build a language model by using a Language Understanding service. The language model is used to search for information on a contact list by using an intent named FindContact.
A conversational expert provides you with the following list of phrases to use for training.
-Find contacts in London.
-Who do I know in Seattle?
Search for contacts in Ukraine.
You need to implement the phrase list in Language Understanding.
Solution: You create a new entity for the domain.
Does this meet the goal?

  1. Yes
  2. No

Answer(s): A

Explanation:

Instead use a new intent for location.
Note: An intent represents a task or action the user wants to perform. It is a purpose or goal expressed in a user's utterance.
Define a set of intents that corresponds to actions users want to take in your application.


Reference:

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



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?

  1. Enable active learning.
  2. Add a machine learned entity.
  3. Add additional examples to the GetContactDetails intent.
  4. Add examples to the None intent.

Answer(s): D

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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