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. (Choose three.)Select and Place:
Answer(s): A
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 modelNote: 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.
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?
Answer(s): B
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?
The model should have an Entity "Location" that will help in finding the contacts.
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?
Answer(s): D
https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/conversational-language-understanding/concepts/none-intent#adding-examples-to-the-none-intent
DRAG DROP (Drag and Drop is not supported)You are building a Language Understanding model for purchasing tickets.You have the following utterance for an intent named PurchaseAndSendTickets. Purchase [2 audit business] tickets to [Paris] [next Monday] and send tickets to [email@domain.com]You need to select the entity types. The solution must use built-in entity types to minimize training data whenever possible.Which entity type should you use for each label? To answer, drag the appropriate entity types to the correct labels. Each entity type may be used once, more than once, or not at all.You may need to drag the split bar between panes or scroll to view content. Select and Place:
Box 1: GeographyV2The prebuilt geographyV2 entity detects places. Because this entity is already trained, you do not need to add example utterances containing GeographyV2 to the application intents.Box 2: EmailEmail prebuilt entity for a LUIS app: Email extraction includes the entire email address from an utterance. Because this entity is already trained, you do not need to add example utterances containing email to the application intents.Box 3: Machine learnedThe machine-learning entity is the preferred entity for building LUIS applications.
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-geographyv2 https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-reference-prebuilt-email https://docs.microsoft.com/en-us/azure/cognitive-services/luis/reference-entity-machine-learned-entity
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