An engineer is building a model to classify text into one of three sentiment categories: 'Positive', 'Negative', or 'Neutral'. For a specific piece of text, the layer just before the final activation function produces the numerical scores [3.5, -2.0, 3.5] for 'Positive', 'Negative', and 'Neutral' respectively. The engineer observes that the final output probabilities for 'Positive' and 'Neutral' are identical and significantly higher than the probability for 'Negative'. Evaluate this observation. Is this the expected behavior? Justify your reasoning by describing how the final activation function processes its input scores to produce a probability distribution.