Microsoft AI-900 Exam Pass Guide, AI-900 Exam Test

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Certification Topics of Microsoft AI-900 Exam

Our Microsoft AI-900 exam dumps covers the following objectives of Microsoft AI-900 Exam.

Describe AI workloads and considerations (15-20%)
Describe features of conversational AI workloads on Azure (15-20%)
Describe fundamental principles of machine learning on Azure (30-35%)
Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%)
Describe features of computer vision workloads on Azure (15-20%)

How to book the AI-900: Microsoft Azure AI Fundamentals Exam

Step 1: Visit to Microsoft Learning and search for AI-900: Microsoft Azure AI Fundamentals Exam Outlook and Modules.
Step 2: Sign up/Login to Pearson VUE account.
Step 3: Select local centre based on your country, date, time and confirm with a payment method.

Microsoft Azure AI Fundamentals Sample Questions (Q218-Q223):

NEW QUESTION # 218
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