Continuous variables are discretized by the Bayes network class. The discretization algorithm chooses its values based on the information in the data set. However, these values are not stored anywhere. So, reading an arff file with continuous variables using the File/Open menu allows one to specify a network, then learn the CPTs from it since the discretization bounds are still known. However, opening an arff file, specifying a structure, then closing the application, reopening and trying to learn the network from another file containing continuous variables may not give the desired result since a the discretization algorithm is re-applied and new boundaries may have been found. Unexpected behavior may be the result.
Learning from a dataset that contains more attributes than there are nodes in the network is ok. The extra attributes are just ignored.
Learning from a dataset with differently ordered attributes is ok. Attributes are matched to nodes based on name. However, attribute values are matched with node values based on the order of the values.
The attributes in the dataset should have the same number of values as the corresponding nodes in the network (see above for continuous variables).