You are creating an educational game that must answer straightforward probability questions like: 'A jar has 6 red, 4 green, and 3 yellow marbles. What is the probability of drawing a green marble?' Which approach meets the requirement with the least operational overhead?

The correct answer is to implement simple rule-based code because calculating straightforward probabilities is a deterministic task. The probability of drawing a green marble can be directly computed using the formula: (number of green marbles) / (total number of marbles). This approach requires minimal development effort, no training data, and no ongoing model maintenance, resulting in the least operational overhead. Training a supervised regression model is unnecessary because there's no complex, non-linear relationship to learn; the answer is directly derivable from the input. Reinforcement learning is designed for sequential decision-making problems, not for calculating static probabilities. Unsupervised learning focuses on finding patterns in unlabeled data and is not suitable for directly computing specific probabilities in a deterministic scenario.