AI-900 Question 187
Select 3You are developing a customer support chatbot using Azure Cognitive Services. The chatbot must understand and process customer feedback written in natural language. Which features of Azure NLP services can you leverage to achieve this?
- A
Sentiment analysis to determine if the customer feedback is positive, neutral, or negative
- B
Named Entity Recognition (NER) to extract specific details like product names or locations mentioned in the feedback
- C
Key phrase extraction to identify the main points or topics of the feedback
- D
Optical Character Recognition (OCR) to convert handwritten feedback into text
- E
Speech synthesis to convert the feedback into voice responses
Show answer and explanation
Correct answers: A, B, C
Explanation
Azure NLP services provide tools like sentiment analysis, named entity recognition, and key phrase extraction to process and understand text-based customer feedback. These features allow the chatbot to analyze customer sentiment, extract important details, and identify key topics, enabling it to respond effectively. OCR and speech synthesis, however, are not part of Azure NLP services and are used for different AI workloads.
- A. Correct.
Sentiment analysis is a key feature of Azure NLP services that allows the chatbot to understand the tone or sentiment of the customer's feedback.
- B. Correct.
Named Entity Recognition (NER) is part of Azure NLP services and can be used to identify specific entities such as product names or locations in the customer's feedback.
- C. Correct.
Key phrase extraction is a feature of Azure NLP services that helps identify the main points or topics in a piece of text, which is useful for understanding customer feedback.
- D. Incorrect.
Optical Character Recognition (OCR) is not part of Azure NLP services; it is a feature of Azure Computer Vision used for reading text from images or handwritten content.
- E. Incorrect.
Speech synthesis is not a feature of NLP; it falls under Azure's Speech service and is used for converting text into spoken audio.