Why 100% accuracy on test data is not good?
I see two ways to go:
1 - There is an error
2 - There is no error.

1 - Look for the error


You probably have commited Data Leakage.  You have added the target in one of the features and the model found out. 
The validation is not right, you have a time series and you have done random validation.
Your test has only a few instances or it is unique.
The test is repeated from the train. 


2 - There is no error

If the prediction is right and you have 100% accuracy, then no need to do Machine Learning. Open the model find where is taking the decision and don't do machine learning, do classical modeling.

For example if your model is a decision tree, just plot it or print it and get the decision and put them yourself.

This sometimes happens when modeling a previous developed algorithm. The new ML model is able to learn what was going on before.