HOT TEST AI-900 FREE | PASS-SURE MICROSOFT AI-900 RELIABLE EXAM REGISTRATION: MICROSOFT AZURE AI FUNDAMENTALS

Hot Test AI-900 Free | Pass-Sure Microsoft AI-900 Reliable Exam Registration: Microsoft Azure AI Fundamentals

Hot Test AI-900 Free | Pass-Sure Microsoft AI-900 Reliable Exam Registration: Microsoft Azure AI Fundamentals

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Microsoft AI-900 (Microsoft Azure AI Fundamentals) Certification Exam is designed to validate the foundational understanding of AI and its applications in Azure. It is an excellent entry-level certification for individuals who want to develop their knowledge of AI and how it can be leveraged in Azure. AI-900 Exam is suitable for anyone interested in AI, including business decision-makers, technical salespeople, developers, and IT professionals.

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Microsoft Azure AI Fundamentals Sample Questions (Q98-Q103):

NEW QUESTION # 98
You need to scan the news for articles about your customers and alert employees when there is a negative article. Positive articles must be added to a press book.
Which natural language processing tasks should you use to complete the process? To answer, drag the appropriate tasks to the correct locations. Each task may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Diagram Description automatically generated

Box 1: Entity recognition
the Named Entity Recognition module in Machine Learning Studio (classic), to identify the names of things, such as people, companies, or locations in a column of text.
Named entity recognition is an important area of research in machine learning and natural language processing (NLP), because it can be used to answer many real-world questions, such as:
Which companies were mentioned in a news article?
Does a tweet contain the name of a person? Does the tweet also provide his current location?
Were specified products mentioned in complaints or reviews?
Box 2: Sentiment Analysis
The Text Analytics API's Sentiment Analysis feature provides two ways for detecting positive and negative sentiment. If you send a Sentiment Analysis request, the API will return sentiment labels (such as "negative",
"neutral" and "positive") and confidence scores at the sentence and document-level.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/named-entity-recognition
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/how-tos/text-analytics-how-to-sentimen


NEW QUESTION # 99
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:
Explanation

Reference:
https://azure.microsoft.com/en-gb/services/cognitive-services/speech-to-text/#features Speech recognition means Speech to Text. In the above example as a person speaks the words are converted into text of the same language. Hence Speech to Text also called Speech recognition is the right answer.
Speech recognition - the ability to detect and interpret spoken input.
Speech synthesis - the ability to generate spoken output.
https://docs.microsoft.com/en-us/learn/modules/recognize-synthesize-speech/1-introduction


NEW QUESTION # 100
You need to track multiple versions of a model that was trained by using Azure Machine Learning. What should you do?

  • A. Register the model.
  • B. Register the training data.
  • C. Explain the model.
  • D. Provision an inference duster.

Answer: A


NEW QUESTION # 101
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation


NEW QUESTION # 102
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.

Answer:

Explanation:

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


NEW QUESTION # 103
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