When the AutoML API consumes data, it validates schema consistency between the train and validation data.

There are two bugs in this logic:

1. The API asserts that the count of columns in the train and validation data must be equal. This throws an exception if the two data views have the same number of active columns but a different number of hidden columns. This should be updated to assert that the # of active (not hidden) columns in the train and validation data are equal.

2. If either the train or validation data has a hidden column with a type that differs from an active column of the same name, an exception is thrown. Type consistency checks should be restricted to active columns only.