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Free AI-900 Exam Braindumps (page: 29)

Page 28 of 63
PDF with 247 Questions

You are developing a chatbot solution in Azure.
Which service should you use to determine a user's intent?

  1. Translator
  2. QnA Maker
  3. Speech
  4. Language Understanding (LUIS)

Answer(s): D

Explanation:

Language Understanding (LUIS) is a cloud-based API service that applies custom machine-learning intelligence to a user's conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.
Design your LUIS model with categories of user intentions called intents. Each intent needs examples of user utterances. Each utterance can provide data that needs to be extracted with machine-learning entities.


Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis



You need to make the written press releases of your company available in a range of languages.
Which service should you use?

  1. Translator
  2. Text Analytics
  3. Speech
  4. Language Understanding (LUIS)

Answer(s): A

Explanation:

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/



HOTSPOT (Drag and Drop is not supported)
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:

  1. See Explanation section for answer.

Answer(s): A

Explanation:




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



DRAG DROP (Drag and Drop is not supported)
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:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



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






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