HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:
You need to identify street names based on street signs in photographs. Which type of computer vision should you use?
Question 114
DRAG DROP - 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. Select and Place:
Box 1: Entity recognition - Named Entity Recognition (NER) is the ability to identify different entities in text and categorize them into pre-defined classes or types such as: person, location, event, product, and organization. Box 2: Sentiment analysis - Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. Box 3: Translation - Using Microsoft's Translator text API This 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://docs.microsoft.com/en-in/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-entity-linking?tabs=version-3-preview https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
Question 115
You plan to develop a bot that will enable users to query a knowledge base by using natural language processing. Which two services should you include in the solution? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
HOTSPOT - For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Hot Area:
The translator service provides multi-language support for text translation, transliteration, language detection, and dictionaries. Speech-to-Text, also known as automatic speech recognition (ASR), is a feature of Speech Services that provides transcription. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/Translator/translator-info-overview https://docs.microsoft.com/en-us/legal/cognitive-services/speech-service/speech-to-text/transparency-note
Question 117
DRAG DROP - 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. Select and Place:
Box 1: Entity recognition - the Named Entity Recognition module in Machine Learning Studio (classic), to identify the names of things, such as people, companies, or locations in a column of text. Named entity recognition is an important area of research in machine learning and natural language processing (NLP), because it can be used to answer many real-world questions, such as: - Which companies were mentioned in a news article? - Does a tweet contain the name of a person? Does the tweet also provide his current location? - Were specified products mentioned in complaints or reviews? Box 2: Sentiment Analysis - The Text Analytics API's Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as "negative", "neutral" and "positive") and confidence scores at the sentence and document-level. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/named-entity-recognition https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentiment-analysis
Question 118
You are authoring a Language Understanding (LUIS) application to support a music festival. You want users to be able to ask questions about scheduled shows, such as: `Which act is playing on the main stage?` The question `Which act is playing on the main stage?` is an example of which type of element?
Utterances are input from the user that your app needs to interpret. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/LUIS/luis-concept-utterance
Question 119
You are building a knowledge base by using QnA Maker. Which file format can you use to populate the knowledge base?
D: Content types of documents you can add to a knowledge base: Content types include many standard structured documents such as PDF, DOC, and TXT. Note: The tool supports the following file formats for ingestion: - .tsv: QnA contained in the format Question(tab)Answer. - .txt, .docx, .pdf: QnA contained as regular FAQ content--that is, a sequence of questions and answers. Incorrect Answers: A: PPTX is the default presentation file format for new PowerPoint presentations. B: It is not possible to ingest xml file directly. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/data-sources-and-content
Question 120
In which scenario should you use key phrase extraction?
