GENERATIVE-AI-LEADER Question #63: Real Exam Question with Answer & Explanation

Question

A company wants to build a model to classify customer reviews as positive, negative, or neutral. They have collected a dataset of thousands of customer reviews, and each review has been manually tagged with the corresponding sentiment: positive, negative, or neutral. What machine learning should the company use?

Options

• ADeep learning
• BUnsupervised learning
• CReinforcement learning
• DSupervised learning

Explanation

The machine learning approach is determined by the nature of the data available and the desired Data Available: Customer reviews (input) that are manually tagged with a sentiment category Desired Output: A model that can classify new, untagged reviews into one of the predefined categories (positive, negative, or neutral). This scenario perfectly aligns with the definition of Supervised Learning (D). Supervised learning is the machine learning paradigm where the model is trained on a labeled dataset--a dataset where the input data is explicitly paired with the correct output label. The model learns a function that maps the input (the review text) to the output (the sentiment tag) and is then used to predict the label for unseen data.

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