A model learns in one of three ways, and which one applies is decided by the data you have rather than by the outcome you want.
Supervised learning
The training data carries the answer. Each example has a label, and the model learns to predict that label for input it has not seen.
Two tasks sit underneath it. Classification predicts a category, such as whether a transaction is fraudulent or which of five topics a support ticket belongs to. Regression predicts a number, such as next month's demand or the price a property will fetch.
Supervised learning is the workhorse, and its constraint is labels. Labels are expensive, they are often produced by people who disagree with each other, and a model can never be better than the labels it learned from.
Unsupervised learning
The data has no labels and the model finds structure on its own. Clustering groups similar records without anybody naming the groups, which is how customer segments emerge from behaviour rather than from assumption. Dimensionality reduction compresses many variables into a few that carry most of the signal. Anomaly detection learns what normal looks like and flags what is not.
The catch is evaluation. With no ground truth, there is no accuracy figure, and judging whether the clusters mean anything is a human job.
Reinforcement learning
An agent acts in an environment and receives rewards or penalties, and it learns a policy that maximises reward over time. There is no labelled dataset at all. There is a goal, a set of possible actions, and feedback.
It suits sequential decisions where each choice changes what comes next, such as robotics, game playing, or bidding. It appears in the syllabus mainly through reinforcement learning from human feedback, the step where people rank model outputs and those rankings train a reward model that shapes a language model's behaviour.
The data underneath
The exam separates data along two lines and expects you to place an example on both.
Labelled or unlabelled decides whether supervised learning is even available to you.
Structured or unstructured decides how it has to be handled. Structured data has a schema, which covers tabular records and time series where order carries meaning. Unstructured data does not, which covers text, images, audio and video, and is where deep learning earns its cost.
Choosing between them
The useful question is not which technique is most powerful, it is which one your situation admits.
If you have labelled examples of the thing you want to predict, use supervised learning. If you have data and no labels and you are looking for structure that might be there, use unsupervised learning. If the problem is a sequence of decisions with delayed consequences, and you can define a reward honestly, use reinforcement learning.
And if you can state the rule in a sentence, write the rule. A model that learns something you already knew costs more and explains less.