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NEW QUESTION 27
Select the answer that correctly completes the sentence.

Answer:

Explanation:
Explanation

NEW QUESTION 28
To complete the sentence, select the appropriate option in the answer area.
Using Recency, Frequency, and Monetary (RFM) values to identify segments of a customer base is an example of___________

Answer:

Explanation:
Seethebelowin
Explanation:
Classification

NEW QUESTION 29
You run a charity event that involves posting photos of people wearing sunglasses on Twitter.
You need to ensure that you only retweet photos that meet the following requirements:
Include one or more faces.
Contain at least one person wearing sunglasses.
What should you use to analyze the images?

• A. the Verify operation in the Face service
• B. the Describe Image operation in the Computer Vision service
• C. the Detect operation in the Face service
• D. the Analyze Image operation in the Computer Vision service

Answer: C

Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview

NEW QUESTION 30
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 31
......