You have insurance claim reports that are stored as text. You need to extract key terms from the reports to generate summaries. Which type of AI workload should you use?
HOTSPOT - To complete the sentence, select the appropriate option in the answer area. Hot Area:
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 123
Predicting agricultural yields based on weather conditions and soil quality measurements is an example of which type of machine learning model?
A. classification
B. regression
C. clustering
Question 124
You are building a tool that will process images from retail stores and identify the products of competitors. The solution must be trained on images provided by your company. Which Azure AI service should you use?
A. Form Recognizer
B. Custom Vision
C. Face
D. Computer Vision
Question 125
You need to make the written press releases of your company available in a range of languages. Which service should you use?
A. Translator
B. Text Analytics
C. Speech
D. Language Understanding (LUIS)
Translator is a cloud-based machine translation service you can use to translate text in near real-time through a simple REST API call. The service uses modern neural machine translation technology and offers statistical machine translation technology. Custom Translator is an extension of Translator, which allows you to build neural translation systems. Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/translator/
Question 126
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 Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection. Box 1: Yes - You can detect which language the input text is written in and report a single language code for every document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural languages. The language code is paired with a score indicating the strength of the score. Box 2: No - Box 3: Yes - 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/cognitive-services/text-analytics/overview
Question 127
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 128
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 - Content Moderator is part of Microsoft Cognitive Services allowing businesses to use machine assisted moderation of text, images, and videos that augment human review. The text moderation capability now includes a new machine-learning based text classification feature which uses a trained model to identify possible abusive, derogatory or discriminatory language such as slang, abbreviated words, offensive, and intentionally misspelled words for review. Box 2: No - Azure's Computer Vision service gives you 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. Box 3: Yes - Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. 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-processing
Question 129
You are developing a natural language processing solution in Azure. The solution will analyze customer reviews and determine how positive or negative each review is. This is an example of which type of natural language processing workload?
A. language detection
B. sentiment analysis
C. key phrase extraction
D. entity recognition
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
Question 130
You use natural language processing to process text from a Microsoft news story. You receive the output shown in the following exhibit. Which type of natural languages processing was performed?
A. entity recognition
B. key phrase extraction
C. sentiment analysis
D. translation
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. In this question, the square brackets indicate the entities such as DateTime, PersonType, Skill. Reference: https://docs.microsoft.com/en-in/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-entity-linking?tabs=version-3-preview
