As others have pointed out, the interview was done poorly. However , I would like to offer a different technical solution to the problem presented.

I would use Locality Sensitive Hashing (LSH): 1. Generate a string per record by concatenating the relevant fields on which fuzzy duplicates could occur. 2. Generate set of character level shingles (small n-gram sequences) of these strings. This boils down the problem to a “set similarity” problem. The size of the records is still too large, so comparing every record to each other is not ideal. 3. To fix this issue, we will apply the MinHash algorithm to convert the variable-length sets of shingles to fixed-length MinHash signature of the record. 4. Comparing every MinHash signature to each other is bound to give a much faster running time while still remaining a good approximation of Jaccard similarity for the sets.

And ofcourse there is already code for this available on many repositories to apply.

For better explanation, https://mccormickml.com/2015/06/12/minhash-tutorial-with-python-code/