The “Cannot resolve ambiguity” message in Power BI most commonly arises when the query engine has several possible routes when moving from one table to another in a model with bidirectional filters. This is largely due to the presence of path relationships or multiple relationships among tables, which lead to conflicting data filters.
In a usual Power BI design, bidirectional relationships welcome the propagation of filters in both axis directions, unlike in Power BI man,y such relationships exist amongst an equal set of tables, which then becomes a challenge in choosing which relationship direction to filter the data. For example, if Table A is linked to Table B and Table C is related to Table B, but again, both relationships are two-way, then BI may have contradictory routes to act in filtering data, which causes haze.
In order to fix this problem, the following methods may be applied:
Decrease Instances of Bi-Directional Relationships Where Possible: Where applicable, it is recommended that tables be filtered using unidirectional filters. This helps simplify the model and reduce any possible confusion.
Turn Off One or More Relationships: If you have many relationships between two tables, turn off the ones that are not needed. You can do this in the Manage Relationships window by setting the relationship to “inactive.” For purposes of a particular calculation, you can call upon the relationship that you need by using DAX functions like USERELATIONSHIP().
Review and Redesign Architecture: Look for dependencies that are cyclical in nature or intricate relationships for no adequate reason. Often, this can be fixed by avoiding needless relationships and/or reconstructing the way tables interact.
Address Filtering Using DAX: At times, this may be required to omit the automatic filtering behavior that creates confusion about the flow of data and ensure the context is altered with custom-created DAX expressions.
By tackling these issues, confusion will be eradicated, and the existing relationships between the tables will be clarified, thereby enhancing the precision of the data model.
