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NEW QUESTION 43 You need to build a chatbot that meets the following requirements: * Supports chit-chat, knowledge base, and multilingual models * Performs sentiment analysis on user messages * Selects the best language model automatically What should you integrate into the chatbot?

• A. Language Understanding, Text Analytics, and QnA Maker
• B. Text Analytics, Translator, and Dispatch
• C. QnA Maker, Language Understanding, and Dispatch
• D. Translator, Speech, and Dispatch

Answer: A Explanation: Explanation Language Understanding: An AI service that allows users to interact with your applications, bots, and IoT devices by using natural language. QnA Maker is a cloud-based Natural Language Processing (NLP) service that allows you to create a natural conversational layer over your data. It is used to find the most appropriate answer for any input from your custom knowledge base (KB) of information. Text Analytics: Mine insights in unstructured text using natural language processing (NLP)-no machine learning expertise required. Gain a deeper understanding of customer opinions with sentiment analysis. The Language Detection feature of the Azure Text Analytics REST API evaluates text input Reference: https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics/ https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/overview/overview   NEW QUESTION 44 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?

• A. No
• B. Yes

Answer: A Explanation: 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   NEW QUESTION 45 You are using a Language Understanding service to handle natural language input from the users of a web-based customer agent. The users report that the agent frequently responds with the following generic response: "Sorry, I don't understand that." You need to improve the ability of the agent to respond to requests. 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.) Answer: ** Explanation: Explanation: Step 1: Add prebuilt domain models as required. Prebuilt models provide domains, intents, utterances, and entities. You can start your app with a prebuilt model or add a relevant model to your app later. Note: Language Understanding (LUIS) provides prebuilt domains, which are pre-trained models of intents and entities that work together for domains or common categories of client applications. The prebuilt domains are trained and ready to add to your LUIS app. The intents and entities of a prebuilt domain are fully customizable once you've added them to your app. Step 2: Enable active learning To enable active learning, you must log user queries. This is accomplished by calling the endpoint query with the log=true querystring parameter and value. Step 3: Train and republish the Language Understanding model The process of reviewing endpoint utterances for correct predictions is called Active learning. Active learning captures endpoint queries and selects user's endpoint utterances that it is unsure of. You review these utterances to select the intent and mark entities for these real-world utterances. Accept these changes into your example utterances then train and publish. LUIS then identifies utterances more accurately. Incorrect Answers: Enable log collection by using Log Analytics Application authors can choose to enable logging on the utterances that are sent to a published application. This is not done through Log Analytics. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-how-to-review-endpoint-utterances#log-user-queries-to-enable-active-learning https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-prebuilt-model   **NEW QUESTION 46 You plan to build a chatbot to support task tracking. You create a Language Understanding service named lu1. You need to build a Language Understanding model to integrate into the chatbot. The solution must minimize development time to build the model. Which four 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 four.) Answer: ** Explanation:   **NEW QUESTION 47 ......