Microsoft Azure AI Fundamentals (AI-900) Practice Exam

What type of AI workload assists in identifying negative sentiments from customer chats?

1. Predictive Analytics

2. Natural Language Processing

3. Supervised Learning

4. Image Processing

The correct answer is: Natural Language Processing

The identification of negative sentiments from customer chats falls under the domain of Natural Language Processing (NLP). NLP is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language. It involves the analysis, understanding, and generation of human language in a valuable way. When assessing customer chats for sentiment, NLP algorithms are employed to analyze the text, understand the context, and determine the sentiment expressed, whether it's positive, negative, or neutral. This process often involves various techniques like sentiment analysis, which uses machine learning models trained on labeled data to recognize patterns associated with different sentiments. In contrast, predictive analytics involves forecasting future outcomes based on historical data and patterns rather than analyzing the sentiment of textual information. Supervised learning is a type of machine learning where models are trained on labeled datasets, which can indeed incorporate sentiment analysis, but it doesn’t specifically denote the application to chat analysis. Image processing is focused on manipulating and analyzing visual data, which is not relevant to identifying sentiments in text. Therefore, Natural Language Processing is the most appropriate choice for this type of workload.