DRAG DROP - You plan to apply Text Analytics API features to a technical support ticketing system. Match the Text Analytics API features to the appropriate natural language processing scenarios. To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:
Box1: Sentiment analysis - Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. Box 2: Broad entity extraction - Broad entity extraction: Identify important concepts in text, including key Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations. Box 3: Entity Recognition - Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
Question 132
DRAG DROP - Match the machine learning models to the appropriate descriptions. To answer, drag the appropriate model from the column on the left to its description on the right. Each model may be used once, more than once, or not at all. NOTE: Each correct match is worth one point.
Question 133
You build a QnA Maker bot by using a frequently asked questions (FAQ) page. You need to add professional greetings and other responses to make the bot more user friendly. What should you do?
You need to develop a chatbot for a website. The chatbot must answer users' questions based on the information in the following documents: - A product troubleshooting guide in a Microsoft Word document - A frequently asked questions (FAQ) list on a webpage Which service should you use to process the documents?
You are building a Language Understanding model for an e-commerce business. You need to ensure that the model detects when utterances are outside the intended scope of the model. What should you do?
The None intent is filled with utterances that are outside of your domain. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/LUIS/luis-concept-intent
Question 136
Which two scenarios are examples of a natural language processing workload? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
Question 137
You have an AI solution that provides users with the ability to control smart devices by using verbal commands. Which two types of natural language processing (NLP) workloads does the solution use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Key phrase extraction is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text "The food was delicious and the staff were wonderful.", key phrase extraction will return the main topics: "food" and "wonderful staff". Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/key-phrase-extraction/overview
Question 138
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:
Box 1: Yes - Azure Cognitive Service for Language provides features including: * Language detection: This pre-configured feature evaluates text, and determines the language it was written in. It returns a language identifier and a score that indicates the strength of the analysis. Box 2: No - Handwritten detection is part of OCR (Optical Character Recognition). Box 3: Yes - Azure Cognitive Service for Language provides features including: * Named Entity Recognition (NER): This pre-configured feature identifies entities in text across several pre-defined categories. Note: Named entity recognition is a natural language processing technique that can automatically scan entire articles and pull out some fundamental entities in a text and classify them into predefined categories. Entities may be, Organizations, Quantities, Monetary values, Percentages, and more. People's names - Company names - Geographic locations (Both physical and political) Product names - Dates and times - Amounts of money - Names of events - Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/overview
Question 139
DRAG DROP - You plan to use Azure Cognitive Services to develop a voice controlled personal assistant app. Match the Azure Cognitive Services to the appropriate tasks. To answer, drag the appropriate service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point. Select and Place:
Box 1: Speech - The Speech service provides speech-to-text and text-to-speech capabilities with an Azure Speech resource. You can transcribe speech to text with high accuracy, produce natural-sounding text-to-speech voices, translate spoken audio, and use speaker recognition during conversations. Box 2: Language service - Build applications with conversational language understanding, a Cognitive Service for Language feature that understands natural language to interpret user goals and extracts key information from conversational phrases. Create multilingual, customizable intent classification and entity extraction models for your domain- specific keywords or phrases across 96 languages. Box 3: Speech - Incorrect: Not Translator text: Text translation is a cloud-based REST API feature of the Translator service that uses neural machine translation technology to enable quick and accurate source-to-target text translation in real time across all supported languages. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/speech-service/overview https://azure.microsoft.com/en-us/services/cognitive-services/conversational-language-understanding/ https://docs.microsoft.com/en-us/azure/cognitive-services/translator/text-translation-overview
Question 140
A smart device that responds to the question “What is the stock price of Contoso. Ltd.?” is an example of which AI workload?
