This page was exported from IT Certification Exam Braindumps [ http://blog.braindumpsit.com ] Export date:Sun Oct 6 12:31:55 2024 / +0000 GMT ___________________________________________________ Title: Get Real AI-900 Exam Dumps [Jul-2023] Practice Tests [Q17-Q36] --------------------------------------------------- Get Real AI-900 Exam Dumps [Jul-2023] Practice Tests Last AI-900 practice test reviews: Practice Test Microsoft dumps Q17. For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point. Reference:https://docs.microsoft.com/en-us/azure/cognitive-services/Translator/translator-info-overviewhttps://docs.microsoft.com/en-us/legal/cognitive-services/speech-service/speech-to-text/transparency-noteQ18. Match the types of natural languages processing workloads to the appropriate scenarios.To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.NOTE: Each correct selection is worth one point. ExplanationBox 1: Entity recognitionClassify a broad range of entities in text, such as people, places, organisations, date/time and percentages, using named entity recognition. Whereas:- Get a list of relevant phrases that best describe the subject of each record using key phrase extraction.Box 2: Sentiment analysisSentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.Box 3: TranslationUsing Microsoft’s Translator text APIThis versatile API from Microsoft can be used for the following:Translate text from one language to another.Transliterate text from one script to another.Detecting language of the input text.Find alternate translations to specific text.Determine the sentence length.Reference:https://azure.microsoft.com/en-us/services/cognitive-services/text-analyticsQ19. For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point. Reference:https://www.cloudfactory.com/data-labeling-guidehttps://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performanceQ20. You need to scan the news for articles about your customers and alert employees when there is a negative article. Positive articles must be added to a press book.Which natural language processing tasks should you use to complete the process? To answer, drag the appropriate tasks to the correct locations. Each task 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.NOTE: Each correct selection is worth one point. Reference:https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/named-entity-recognitionhttps://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentiment-analysisQ21. You need to provide content for a business chatbot that will help answer simple user queries.What are three ways to create Question: 57solution.NOTE: Each correct selection is worth one point.  Generate the Questions and answers from an existing webpage.  Use automated machine learning to train a model based on a file that contains the Question:s.  Manually enter the Questions and answers.  Connect the bot to the Cortana channel and ask Questions by using Cortana.  Import chit-chat content from a predefined data source. Extract question answer pairs from semi-structured content, including FAQ pages, support websites, excel files, SharePoint documents, product manuals and policies.Reference:https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/content-typesQ22. For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point. Reference:https://azure.microsoft.com/es-es/blog/machine-assisted-text-classification-on-content-moderator-public-preview/https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processingQ23. For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point. Reference:https://docs.microsoft.com/en-us/azure/machine-learning/concept-designerQ24. During the process of Machine Learning, when should you review evaluation metrics?  After you clean the data.  Before you train a model.  Before you choose the type of model.  After you test a model on the validation data. Q25. To complete the sentence, select the appropriate option in the answer area. ExplanationDiagram, table Description automatically generatedReference:https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-aiQ26. You have the process shown in the following exhibit.Which type AI solution is shown in the diagram?  a sentiment analysis solution  a chatbot  a machine learning model  a computer vision application Q27. For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point. Reference:https://docs.microsoft.com/en-gb/azure/cognitive-services/text-analytics/overviewhttps://azure.microsoft.com/en-gb/services/cognitive-services/speech-services/ You can use the Speech service to transcribe a call to text – Yes we can use Speech to Text API to achieve thishttps://docs.microsoft.com/en-us/learn/modules/recognize-synthesize-speech/1-introduction You can use a speech service to translate the audio of a call to a different language – Yes we can use Speech translation service to achieve this The Speech service includes the following application programming interfaces (APIs):Speech-to-text – used to transcribe speech from an audio source to text format.Text-to-speech – used to generate spoken audio from a text source.Speech Translation – used to translate speech in one language to text or speech in another.https://docs.microsoft.com/en-us/learn/modules/translate-text-with-translation-service/2-get-started-azure You can use text analytics service to extract key entities from a call transcript -Yes Text Analytics API helps to achieve thishttps://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azureQ28. You use Azure Machine Learning designer to publish an inference pipeline.Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.  the model name  the training endpoint  the authentication key  the REST endpoint https://docs.microsoft.com/en-in/learn/modules/create-regression-model-azure-machine-learning-designer/deploy-serviceQ29. For each of the following statements, select Yes if the statement is true. Otherwise, select No.NOTE: Each correct selection is worth one point. Reference:https://docs.microsoft.com/en-us/azure/cognitive-services/Translator/translator-info-overviewhttps://docs.microsoft.com/en-us/legal/cognitive-services/speech-service/speech-to-text/transparency-noteQ30. You are developing a model to predict events by using classification.You have a confusion matrix for the model scored on test data as shown in the following exhibit.Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.NOTE: Each correct selection is worth one point. ExplanationBox 1: 11TP = True Positive.The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative. The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively. Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).Box 2: 1,033FN = False NegativeReference:https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance Finding TP is easy. It basically means the value where Predicted and True value is 1 and that is 11 in this case.False Negative means where true value was 1 but predicted value was 0 and that is 1033 in this case The confusion matrix shows cases where both the predicted and actual values were 1 (known as true positives) at the top left, and cases where both the predicted and the actual values were 0 (true negatives) at the bottom right. The other cells show cases where the predicted and actual values differ (false positives and false negatives).https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/evaQ31. To complete the sentence, select the appropriate option in the answer area. Reference:https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#featuresQ32. You need to provide content for a business chatbot that will help answer simple user queries.What are three ways to create question and answer text by using QnA Maker? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.  Generate the questions and answers from an existing webpage.  Use automated machine learning to train a model based on a file that contains the questions.  Manually enter the questions and answers.  Connect the bot to the Cortana channel and ask questions by using Cortana.  Import chit-chat content from a predefined data source. Section: Describe features of conversational AI workloads on AzureExplanation:Automatic extractionExtract question-answer pairs from semi-structured content, including FAQ pages, support websites, excel files, SharePoint documents, product manuals and policies.Reference:https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/content-typesQ33. To complete the sentence, select the appropriate option in the answer area. Reference:https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasksQ34. Match the Microsoft guiding principles for responsible AI to the appropriate descriptions.To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all.NOTE: Each correct selection is worth one point. ExplanationBox 1: Reliability and safetyTo build trust, it’s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.Box 2: accountabilityBox 3: Privacy and securityAs AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is usedhttps://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principlesQ35. What are two tasks that can be performed by using the Computer Vision service? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.  Train a custom image classification model.  Detect faces in an image.  Recognize handwritten text.  Translate the text in an image between languages. B: Azure’s Computer Vision service provides developers with access to advanced algorithms that process images and return information based on the visual features you’re interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.C: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents.Reference:https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/home Detect faces in an image – Face API Microsoft Azure provides multiple cognitive services that you can use to detect and analyze faces, including:Computer Vision, which offers face detection and some basic face analysis, such as determining age.Video Indexer, which you can use to detect and identify faces in a video.Face, which offers pre-built algorithms that can detect, recognize, and analyze faces.Recognize hand written text – Read APIThe Read API is a better option for scanned documents that have a lot of text. The Read API also has the ability to automatically determine the proper recognition modelQ36. For a machine learning progress, how should you split data for training and evaluation?  Use features for training and labels for evaluation.  Randomly split the data into rows for training and rows for evaluation.  Use labels for training and features for evaluation.  Randomly split the data into columns for training and columns for evaluation. ExplanationIn Azure Machine Learning, the percentage split is the available technique to split the data. In this technique, random data of a given percentage will be split to train and test data.Reference:https://www.sqlshack.com/prediction-in-azure-machine-learning/ Loading … Get Ready to Pass the AI-900 exam with Microsoft Latest Practice Exam : https://www.braindumpsit.com/AI-900_real-exam.html --------------------------------------------------- Images: https://blog.braindumpsit.com/wp-content/plugins/watu/loading.gif https://blog.braindumpsit.com/wp-content/plugins/watu/loading.gif --------------------------------------------------- --------------------------------------------------- Post date: 2023-07-17 15:05:29 Post date GMT: 2023-07-17 15:05:29 Post modified date: 2023-07-17 15:05:29 Post modified date GMT: 2023-07-17 15:05:29