A situation that validation accuracy is higher than test accuracy at its minimum loss region
Assuming the goal is to give the true performance of the model, then it should be the performance obtained by applying the final model on the test set. The fact that the performance on the validation set can be observed during training is irrelevant.
So the question becomes: which model should you select based on observing the performance on the validation set during training? You can select the model which gives the maximum accuracy, but the real performance is obtained when you evaluate it on a fresh test set.
The second question depends on which performance measure you want to optimize: if you want to optimize accuracy, then you should pick the model which obtains the highest accuracy.