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Free AI-900 Exam Braindumps (page: 32)

Page 31 of 63
PDF with 247 Questions

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.

  1. monitoring the temperature of machinery to turn on a fan when the temperature reaches a specific threshold
  2. a smart device in the home that responds to questions such as, "What will the weather be like today?"
  3. a website that uses a knowledge base to interactively respond to users' questions
  4. assembly line machinery that autonomously inserts headlamps into cars

Answer(s): B,C

Explanation:

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



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.

  1. text-to-speech
  2. key phrase extraction
  3. speech-to-text
  4. language modeling
  5. translation

Answer(s): C,D

Explanation:

Considering the scenario described where the goal is to control smart devices using voice commands, the most appropriate choice would be to use speech-to-text conversion as the first step in the process and then apply language modeling to generate consistent and meaningful responses or actions based on the commands recognized in the produced text. This could allow the AI to understand the users' intent and respond appropriately.
Key phrase extraction could also work but is more complex because an additional layer would have to be added that understands user intent based on the combination of keywords extracted. But it would become complex and probably less efficient as well. Language modeling solves this problem natively.



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:



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



DRAG DROP (Drag and Drop is not supported)
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:

  1. See Explanation section for answer.

Answer(s): A

Explanation:



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






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