I have solved the issue through trial and error and continuing to learn about fuzzy match and the intricacies.
Short version: You don't have to have the top and bottom portion of the Fuzzy Match tool completed. You only need to have 1 variable that matches for a Key Match. This turned out to be the trick that did it for me. I have a "set" of data being compiled by my first run through a fuzzy match, i then do an exact match type on those known variables (SourceID & MatchCandidate ID). The fuzzy match occurs using "Character - Levenshtein Distance" for the field in question (Postal Code in example). I have attached a picture to help illustrate my options.
Source ID & MC ID i have relabeled for my sanity but were originally named "Record ID1" & "Record ID2".
Basically, I take all the records concerned, pass them through a generic Name field only match. this returns a set of data that i want to further scrutenize, so i manipulate the data and output to go back with the match candidates from Name and individually run them through a 'lite' fuzzy match process and return a score, that score then gets joined back to the original data.
If you know anything about fuzzy matches, you know that it works down the columns, not across rows. I selected my original output twice and unioned that output, then mixed the fields on the union, assigned a "TempRecordID" for the lite fuzzy match process and rejoin on ONE side since they were duplicated then rejoin that output using the Make Group, this is why records go from 5,002 to 10,004 back down to 5,002.
Hope it helps!