AI-102 exam dumps

AI-102 practice question 173 of 493

Designing and Implementing a Microsoft Azure AI Solution. Professional level, Microsoft. Free question with the correct answer and a full explanation.

AI-102 Question 173

Select 2

Your organization needs to analyze customer feedback collected via emails to identify key themes and sentiments. You plan to use Azure AI Language for this task. Which combination of Azure AI Language features would be most appropriate to extract the required insights?

  1. A

    Sentiment Analysis

  2. B

    Key Phrase Extraction

  3. C

    Named Entity Recognition (NER)

  4. D

    Language Detection

  5. E

    Text Translation

Show answer and explanation

Correct answers: A, B

Explanation

To analyze customer feedback effectively, Sentiment Analysis and Key Phrase Extraction are the most suitable Azure AI Language features. Sentiment Analysis identifies customer opinions, while Key Phrase Extraction highlights the main themes or topics in the feedback. Other options like Named Entity Recognition, Language Detection, and Text Translation are not directly aligned with the stated goal of identifying themes and sentiments.

  • A. Correct.

    Sentiment Analysis is used to determine the overall sentiment (positive, negative, neutral) of the customer feedback, making it relevant for understanding customer opinions.

  • B. Correct.

    Key Phrase Extraction helps identify important themes or topics mentioned in the feedback, which is essential for summarizing customer concerns or interests.

  • C. Incorrect.

    Named Entity Recognition (NER) identifies specific entities like names, dates, and locations, which is not required for extracting themes or sentiments in this scenario.

  • D. Incorrect.

    Language Detection identifies the language of the text, but this is not directly related to analyzing sentiment or extracting key themes.

  • E. Incorrect.

    Text Translation is used to translate text from one language to another, which is not relevant for analyzing customer feedback in this scenario.

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