| Question.1 HOTSPOT – You are building a solution that students will use to find references for essays. You use the following code to start building the solution.  For each of the following statements, select Yes is the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.  (A) tableName (B) generatedKeyName (C) dataSource (D) dataSourceConnection (E) source |
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Correct Answer : A, B, E
Exaplanation : Defining a table projection. Each table requires three properties: ? tableName: The name of the table in Azure Storage. ? generatedKeyName: The column name for the key that uniquely identifies this row. ? source: The node from the enrichment tree you are sourcing your enrichments from. This node is usually the output of a shaper, but could be the output of any of the skills. Reference: https://docs.microsoft.com/en-us/azure/search/knowledge-store-projection-overview
| Question.2 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) Yes (B) No |
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Correct Answer : A
Exaplanation : A is the answer. We create a new location entity for domain to keep the location of FindContact intent. https://learn.microsoft.com/en-us/azure/cognitive-services/luis/how-to/entities Create entities to extract key data from user utterances in Language Understanding (LUIS) apps. Extracted entity data is used by your client application to fulfill customer requests. The entity represents a word or phrase inside the utterance that you want extracted. Entities describe information relevant to the intent, and sometimes they are essential for your app to perform its task.
| Question.3 You plan to build an app that will generate a list of tags for uploaded images. The app must meet the following requirements: • Generate tags in a user’s preferred language. • Support English, French, and Spanish. • Minimize development effort. You need to build a function that will generate the tags for the app. Which Azure service endpoint should you use? (A) Content Moderator Image Moderation (B) Custom Vision image classification (C) Computer Vision Image Analysis (D) Custom Translator |
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Correct Answer : C
Exaplanation : C, because of the minimized developement effort. Since the prebuilt model of C also fits the other two requirements, so there is no need to train a custom model. source: https://learn.microsoft.com/en-us/azure/cognitive-services/computer-vision/how-to/call-analyze- image?tabs=rest
| Question.4 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 are building a chatbot that will use question answering in Azure Cognitive Service for Language. You have a PDF named Doc1.pdf that contains a product catalogue and a price list. You upload Doc1.pdf and train the model. During testing, users report that the chatbot responds correctly to the following question: What is the price of ? The chatbot fails to respond to the following question: How much does cost? You need to ensure that the chatbot responds correctly to both questions. Solution: From Language Studio, you create an entity for price, and then retrain and republish the model. Does this meet the goal? (A) Yes (B) No |
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Correct Answer : B
Exaplanation : No Creating an entity for price and retraining the model in Language Studio is not the correct approach to solve the issue with Azure Cognitive Service for Language’s question-answering capabilities. Instead, you should use Language Studio to create and train a synonym for the term “price” or build a more comprehensive list of question variations that the chatbot should be able to handle. For example, you can include phrases like “How much does it cost?” or “What is the cost of?” to ensure the model can properly recognize and respond to different ways users might ask about the price. Retrain and republish the model after making these changes to improve the chatbot’s ability to answer both questions correctly.
| Question.5 You have a Conversational Language Understanding model. You export the model as a JSON file. The following is a sample of the file. What represents the Weather.Historic entity in the sample utterance? (A) last year (B) by month (C) amount of (D) average |
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Correct Answer : B
Exaplanation : by month