Sql-server – Can’t modify FormatString on cube ( SSAS )

You can try the following calculation:

Openings:=
CALCULATE( SUM( 'Requisition Counts'[NumberofOpeningsQT] )
         , CALCULATETABLE( LASTNONBLANK( 'Requisition Counts'[SnapshotDateKEY] , 1 )
                         , ALL( 'Requisition Status' )
                         )
         )

The important part is in setting the correct filter context for the outer CALCULATE.

With CALCULATETABLE, we define the specific rows which we want the outer CALCULATE to consider: the last non-blank snapshot date for all requisitions (or for a specific requisition, considering the context of each requisition on a pivot table), regardless of any filters in the requisition status, which is achieved using the ALL function.

The measure doesn't work exactly as you want; you say that you would expect the total for Approved to be 2. Using the 2013-11059 requisition from the Power Pivot model as an example, since there is 1 Approved status in 2013-07-01 and another in 2013-07-02, it just considers the last status for the total, resulting in 1.

What you are seeing is a typical example of a missing dimension relationship.

Yes it has something to do with the relations in your dsv but easy to solve with dimension relationships.

If you add a dimension to a measure group/cube/fact table the dimension relations are automatically generated based on the relations in the data source view, if a relation isn't present a dimension relation has to be created manually.

So in your example you need to go to the cube where the measure group is residing and select dimension relationships and define the relation between your dimension key and the fact table.

See defining dimension relationships for details on how to do exactly that.

It's actually fairly obvious why SSAS would behave like that. If there is no relation between axis and figures, there is no way a slicer can have impact on the figures.

If you are using a Tabular model, unless it lets you add relations in both directions I fear you'll be out of luck and need to design your data warehouse better using actual fact tables and dimension tables.