A used-car marketplace wants to help sellers set an asking price. It has a database of past listings that already includes the final sale price alongside mileage, age, and condition rating for each vehicle. The company wants a model that, given a new car's mileage, age, and condition, outputs a suggested price. Which type of machine learning problem is this?

The correct answer is supervised learning, because every historical listing in the dataset already carries the outcome the model is meant to predict, the actual final sale price, alongside the input features of mileage, age, and condition. Training a model on inputs paired with known correct outputs is the definition of supervised learning, and since the target is a continuous price, it is specifically a regression task within that category. The unsupervised-learning choice, which says the model must discover pricing patterns without being told the answer, is incorrect because unsupervised learning is used when there are no labeled outcomes at all, such as grouping listings by similarity without knowing any sale prices, which does not match this dataset. The reinforcement-learning choice, which says the model improves its pricing by trial and error over repeated listings, is incorrect because reinforcement learning requires an agent taking actions in an environment and receiving reward signals over time, not a static table of past sales with known prices attached. The semi-supervised-learning choice, which says only some of the past listings include a final sale price, is incorrect because the scenario states the database already includes the final sale price for each past example, meaning every record is labeled, not just a partial subset, so there is no missing-label condition to justify a semi-supervised approach.