AI-102 practice questions 131–140

1. Question 131 · 328 in bank

   Select 2

   You have a chatbot that was built by using the Microsoft Bot Framework. You need to debug the chatbot endpoint remotely. Which two tools should you install on a local computer? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

   • AFiddler
   • BBot Framework Composer
   • CBot Framework Emulator
   • DBot Framework CLI
   • Engrok
   • Fnginx

   Show answer

   Answer: CE

   Explanation

   C. Bot Framework Emulator: This is an essential tool for debugging Microsoft Bot Framework bots. It allows you to test and debug your bots on your local machine by emulating the Bot Framework's channels and activities. It can be very helpful in a local development environment but is less suited for remote debugging. E. ngrok: ngrok is a tool that creates a secure tunnel to your localhost. This is very useful for remote debugging because it allows you to expose your local development server to the internet, which is necessary for testing and debugging interactions with services like the Microsoft Bot Framework.

   Source: Adapted from community discussion — evangelist

   References

   • Microsoft Learn
   • Microsoft Learn
   • Microsoft Learn

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2. Question 132 · 328 in bank

   DRAG DROP - 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:

   

   This is an interactive task on the real exam — a drag-and-drop, hotspot or ordering question. There is nothing to select here, so the answer is shown below for reference.

   Show the answer figure

   Answer: See figure below

   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

   Source: Adapted from community discussion — zellck

   References

   • Microsoft Learn

   

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3. Question 133 · 328 in bank

   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?

   • AYes
   • BNo

   Show answer

   Answer: B

   Explanation

   The answer should be NO. An utterance having wo intents? This is illogical. The model should have an Entity "Location" that will help in finding the contacts. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/luis/luis-concept-intent

   Source: Adapted from community discussion — zubfaruq

   References

   • Microsoft Learn

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4. Question 134 · 328 in bank

   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?

   • AYes
   • BNo

   Show answer

   Answer: B

   Explanation

   B. No Creating a new entity for the domain does not directly address the goal of implementing the provided phrase list for the "FindContact" intent. Entities are typically used to extract specific pieces of information from user input, like names, locations, etc. In this case, the goal is to improve the understanding of the intent "FindContact" by providing training phrases related to searching for contacts in specific locations. Instead of creating a new entity, you should focus on training the language model within the existing "FindContact" intent and include the provided phrases to enhance its ability to recognize and understand user queries related to finding contacts in different locations.

   Source: Adapted from community discussion — abbey127

   References

   • Microsoft Learn

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5. Question 135 · 328 in bank

   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 develop an application to identify species of flowers by training a Custom Vision model. You receive images of new flower species. You need to add the new images to the classifier. Solution: You add the new images, and then use the Smart Labeler tool. Does this meet the goal?

   • AYes
   • BNo

   Show answer

   Answer: B

   Explanation

   The answer is B is because the limitations of the smart labeler: You should only request suggested tags for images whose tags have already been trained on once. Don't get suggestions for a new tag that you're just beginning to train. You are given new images of species that have not been seen by the model how can you expect it to suggest what they are? Also you can train the model right in the smart labeler: check the workflow and the limitations in the doc. https://learn.microsoft.com/en-us/azure/ai-services/custom-vision-service/suggested-tags

   Source: Adapted from community discussion — sl_mslconsulting

   References

   • Microsoft Learn
   • Microsoft Learn

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6. Question 136 · 328 in bank

   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?

   • AEnable active learning.
   • BAdd a machine learned entity.
   • CAdd additional examples to the GetContactDetails intent.
   • DAdd examples to the None intent.

   Show answer

   Answer: D

   Explanation

   D is the answer. https://learn.microsoft.com/en-us/azure/cognitive-services/language-service/conversational-language-understanding/concepts/none-intent#adding-examples-to-the-none-intent The None intent is also treated like any other intent in your project. If there are utterances that you want predicted as None, consider adding similar examples to them in your training data. For example, if you would like to categorize utterances that are not important to your project as None, such as greetings, yes and no answers, responses to questions such as providing a number, then add those utterances to your intent. You should also consider adding false positive examples to the None intent. For example, in a flight booking project it is likely that the utterance "I want to buy a book" could be confused with a Book Flight intent. Adding "I want to buy a book" or "I love reading books" as None training utterances helps alter the predictions of those types of utterances towards the None intent instead of Book Flight.

   Source: Adapted from community discussion — zellck

   References

   • Microsoft Learn
   • Microsoft Learn
   • Microsoft Learn

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7. Question 137 · 328 in bank

   DRAG DROP - 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:

   

   This is an interactive task on the real exam — a drag-and-drop, hotspot or ordering question. There is nothing to select here, so the answer is shown below for reference.

   Show the answer figure

   Answer: See figure below

   Explanation

   Box 1: GeographyV2 - The 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: Email - Email 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 learned - The machine-learning entity is the preferred entity for building LUIS applications. Reference: 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

   Source: Adapted from community discussion — zellck

   References

   • Microsoft Learn
   • Microsoft Learn
   • Microsoft Learn
   • Microsoft Learn
   • Microsoft Learn

   

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8. Question 138 · 328 in bank

   You have the following C# method.

   

   You need to deploy an Azure resource to the East US Azure region. The resource will be used to perform sentiment analysis. How should you call the method?

   • Acreate_resource("res1", "ContentModerator", "S0", "eastus")
   • Bcreate_resource("res1", "TextAnalytics", "S0", "eastus")
   • Ccreate_resource("res1", "ContentModerator", "Standard", "East US")
   • Dcreate_resource("res1", "TextAnalytics", "Standard", "East US")

   Show answer

   Answer: B

   Explanation

   Is there any way to get this entire Q & A for free? I'm unable to go past page #23, even though I've signed up for an account.

   Source: Adapted from community discussion — SaviB

   References

   • Microsoft Learn
   • Microsoft Learn

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9. Question 139 · 328 in bank

   You build a Conversational Language Understanding model by using the Language Services portal. You export the model as a JSON file as shown in the following sample.

   

   To what does the Weather.Historic entity correspond in the utterance?

   • Aby month
   • Bchicago
   • Crain
   • Dlocation

   Show answer

   Answer: A

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10. Question 140 · 328 in bank

    SIMULATION - You need to configure and publish bot12345678 to support task management. The intent must be named TaskReminder. The LUDown for the intent is in the C: \Resources\LU folder. To complete this task, use the Microsoft Bot Framework Composer.

    This is an interactive task on the real exam — a drag-and-drop, hotspot or ordering question. There is nothing to select here, so the answer is shown below for reference.

    Show the answer figure

    Answer: See figure below

    Explanation

    Step 1: Open Microsoft Bot Framework Composer Step 2: Select the bot bot12345678 Step 3: Select Import existing resources. Read the instructions on the right side of the screen and select Next. Step 4: Browse to the C:\Resources\LU folder and select the available .lu file Step 5: In the pop-up window Importing existing resources, modify the JSON file content based on your resources information: Name the intent TaskReminder Step 6: Select Publish from the Composer menu. In the Publish your bots pane, select the bot to publish (bot12345678), then select a publish profile from the Publish target drop-down list. Reference: https://docs.microsoft.com/en-us/composer/how-to-publish-bot

    

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AI-102 practice, question 131. 328 questions available.