Exam AI-102 topic 4 question 8 discussion

I also thought it was B, but now I think given answer is correct: Extracted from here https://docs.microsoft.com/en-us/azure/cognitive-services/language-service/key-phrase-extraction/tutorials/integrate-power-bi#create-the-word-cloud The key phrases provide us with the important words from our customer comments, not just the most common words. Also, word sizing in the resulting cloud isn't skewed by the frequent use of a word in a relatively small number of comments.

correct

https://learn.microsoft.com/en-us/azure/ai-services/language-service/key-phrase-extraction/tutorials/integrate-power-bi#create-the-word-cloud "Why use extracted key phrases to generate a word cloud, rather than the full text of every comment? The key phrases provide us with the important words from our customer comments, not just the most common words. Also, word sizing in the resulting cloud isn't skewed by the frequent use of a word in a relatively small number of comments." A for sure

Selected Answer: A

In my opinion, this is only option A.

It must be A.

According to Windows Copilot: The best choice for this scenario is **A. keyPhrases**. This endpoint extracts the main points or topics from a text document, which can be used to generate a word cloud that shows the most frequent or relevant terms in the reviews. The other endpoints are not suitable for this task because: - B. sentiment analyzes the positive, negative, or neutral tone of a text document, which is not relevant for generating a word cloud. - C. languages detects the language of a text document, which is not relevant for generating a word cloud. - D. entities/recognition/general identifies and categorizes named entities in a text document, such as person names, locations, organizations, etc. This might not capture the main points or topics of the reviews, and might include irrelevant or sensitive information in the word cloud.

I think the correct answer is B: Sentiment Analysis and opinion mining: Sentiment analysis and opinion mining are preconfigured features that help you find out what people think of your brand or topic by mining text for clues about positive or negative sentiment, and can associate them with specific aspects of the text. https://learn.microsoft.com/en-us/azure/ai-services/language-service/overview#sentiment-analysis-and-opinion-mining

correct answer. Here is an example: https://learn.microsoft.com/en-us/azure/ai-services/language-service/key-phrase-extraction/tutorials/integrate-power-bi#create-the-word-cloud

A is the answer. https://learn.microsoft.com/en-us/azure/cognitive-services/language-service/key-phrase-extraction/overview Key phrase extraction is one of the features offered by Azure Cognitive Service for Language, a collection of machine learning and AI algorithms in the cloud for developing intelligent applications that involve written language. Use key phrase extraction to quickly identify the main concepts in text. For example, in the text "The food was delicious and the staff were wonderful.", key phrase extraction will return the main topics: "food" and "wonderful staff".

This was on exam 28/06

A. keyPhrases The keyPhrases endpoint extracts key phrases from the given text, which can be useful for creating a word cloud. Key phrases represent the main points or topics discussed in the text, making them ideal for visualizing the most prominent themes in the reviews.

Agreed with A

correct

A is correct answer.

Answer is A. keyPhrases Word Cloud is a set of most frequently appeared words(actually an image) https://docs.microsoft.com/en-us/rest/api/cognitiveservices-textanalytics/3.0/key-phrases/key-phrases

I support "A" as answer as question is asking to develop a "Word cloud based" solution by reviewing of Company's product. (initially, I was thinking about "B" as an answer but I changed my mind.)