Bias or variance

Bias is error from a model that cannot represent the shape, so training and test both sit high. Variance is error from a model that memorizes the sample, so training looks excellent and the test falls apart.

Model A has 2% training error and 30% test error. Model B has 28% training error and 30% test error. Name each one.