Microsoft Azure AI Fundamentals v1.0 (AI-900)

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Total 85 questions

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

  • A. Translator Text
  • B. Text Analytics
  • C. Speech
  • D. Language Understanding (LUIS)


Answer : 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 -
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:




Answer :

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 -
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:




Answer :

Explanation:

Box 1: Key phrase extraction -
Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.

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.
Incorrect Answers:
Not Natural language processing (NLP), which is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Reference:
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:




Answer :

Explanation:

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

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


Answer : B

Explanation:
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

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


Answer : B

Explanation:
Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics

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:




Answer :

Explanation:

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

You are developing a solution that uses the Text Analytics service.
You need to identify the main talking points in a collection of documents.
Which type of natural language processing should you use?

  • A. entity recognition
  • B. key phrase extraction
  • C. sentiment analysis
  • D. language detection


Answer : B

Explanation:
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.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

In which two scenarios can you use speech recognition? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  • A. an in-car system that reads text messages aloud
  • B. providing closed captions for recorded or live videos
  • C. creating an automated public address system for a train station
  • D. creating a transcript of a telephone call or meeting


Answer : BD

Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features

HOTSPOT -
To complete the sentence, select the appropriate option in the answer area.
Hot Area:




Answer :

Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features

You need to build an app that will read recipe instructions aloud to support users who have reduced vision.
Which version service should you use?

  • A. Text Analytics
  • B. Translator Text
  • C. Speech
  • D. Language Understanding (LUIS)


Answer : C

Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/text-to-speech/#features

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:




Answer :

Reference:
https://docs.microsoft.com/en-gb/azure/cognitive-services/text-analytics/overview https://azure.microsoft.com/en-gb/services/cognitive-services/speech-services/

Which two scenarios are examples of a conversational AI workload? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  • A. a telephone answering service that has a pre-recorder message
  • B. a chatbot that provides users with the ability to find answers on a website by themselves
  • C. telephone voice menus to reduce the load on human resources
  • D. a service that creates frequently asked questions (FAQ) documents by crawling public websites


Answer : BC

Explanation:
B: A bot is an automated software program designed to perform a particular task. Think of it as a robot without a body.
C: Automated customer interaction is essential to a business of any size. In fact, 61% of consumers prefer to communicate via speech, and most of them prefer self-service. Because customer satisfaction is a priority for all businesses, self-service is a critical facet of any customer-facing communications strategy.
Incorrect Answers:
D: Early bots were comparatively simple, handling repetitive and voluminous tasks with relatively straightforward algorithmic logic. An example would be web crawlers used by search engines to automatically explore and catalog web content.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/ai-overview https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/interactive-voice-response-bot

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:




Answer :

Explanation:

Box 1: Yes -
Azure bot service can be integrated with the powerful AI capabilities with Azure Cognitive Services.

Box 2: Yes -
Azure bot service engages with customers in a conversational manner.

Box 3: No -
The QnA Maker service creates knowledge base, not question and answers sets.
Note: You can use the QnA Maker service and a knowledge base to add question-and-answer support to your bot. When you create your knowledge base, you seed it with questions and answers.
Reference:
https://docs.microsoft.com/en-us/azure/bot-service/bot-builder-tutorial-add-qna

You need to provide content for a business chatbot that will help answer simple user queries.
What are three ways to create question and answer text by using QnA Maker? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  • A. Generate the questions and answers from an existing webpage.
  • B. Use automated machine learning to train a model based on a file that contains the questions.
  • C. Manually enter the questions and answers.
  • D. Connect the bot to the Cortana channel and ask questions by using Cortana.
  • E. Import chit-chat content from a predefined data source.


Answer : ACE

Explanation:

Automatic extraction -
Extract question-answer pairs from semi-structured content, including FAQ pages, support websites, excel files, SharePoint documents, product manuals and policies.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/content-types

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Total 85 questions