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Which AI service can you use to interpret the meaning of a user input such as `Call me back later?`
Answer: D
Even though LUIS is scheduled to be retired and separate services are now consolidated under Azure AI Language umbrella, curriculum of both AI 900 and AI 102 are not updated as such to reflect exclusion of LUIS and other language services. So better to still learn these services.
Source: Adapted from community discussion — propanther
You are developing a chatbot solution in Azure. Which service should you use to determine a user's intent?
Answer: D
I just took the exam today. 8/19/2022. LUIS is not even on the list of answers. It's now replaced by "Language Service". To my understanding based on the actual exam I took today. Language Service covers as a whole (LUIS, Speech recognition, Speech, Text analytics, NLP, entity recognition,). I passed the exam with score of 916/1000.
Source: Adapted from community discussion — idioteque
You are building an AI system. Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI?
Answer: C
C is the answer. https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai#transparency Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way. Snapshots within Azure Machine Learning workspaces support transparency by recording or retraining all training-related assets and metrics involved in the experiment.
Source: Adapted from community discussion — zellck
You need to make the written press releases of your company available in a range of languages. Which service should you use?
Answer: A
Why do you keep mentioning in your comment that the answer appeared on exam 3.3.2022? Does that exam have 1,000 questions and answers? Stop shit, please, because you confuse others and lead them to fail.
Source: Adapted from community discussion — salihGamar
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:
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.
Answer: See figure below
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
Source: Adapted from community discussion — rdemontis
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:
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.
Answer: See figure below
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
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:
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.
Answer: See figure below
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
Source: Adapted from community discussion — ticbab
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?
Answer: B
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?
Answer: A
Entity recognition has category classification, key phrase extraction does not. Take a look at Key Phrase extraction results: [https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-keyword-extraction] with NER results: [https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-entity-linking?tabs=version-3-preview]
Source: Adapted from community discussion — digitallycan
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:
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.
Answer: See figure below
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
Source: Adapted from community discussion — adelino13
Keyboard:J Kmove between questionsA–Dselect an answerRshow or hide the answer← →change page
AI-900 practice, question 11. 236 questions available.
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