The correct option is Local feature importance.

Local feature importance provides an explanation for a single prediction and shows how each feature contributed to that specific output. This type of explanation gives attribution scores for the instance you care about so you can see whether a particular feature increased or decreased the predicted value.

Global feature importance is incorrect because it summarizes feature effects across the whole dataset and does not tell you how a feature influenced one particular prediction.

Partial dependence plot is incorrect because it shows the average relationship between a feature and the prediction across many samples and it is not a per instance attribution method.

Label influence analysis is incorrect because it focuses on how training labels or training examples affect model behavior and not on attributing an individual prediction to its features.

Full AWS Practitioner Certification Question