To me the way your teacher described Calculus as something that differiantiates two job candidates that are equally qualified it seems more like an Order Winner, but you then went on to say you should narrow your whole group of candidates by that. You shouldn’t cut down your whole pool of candidates by an Order Winner, but by an Order Qualifier. As an example if you’re hiring web developers they may pass an Order Qualifier if they have necessary web development skills. The Order Winner might be that they have good people skills. That may be backwards depending on what you need gaurenteed more. If you feel they can be trained you can certainly do it the other way around. It all depends on what you feel qualifies them and what differentiates them in the final choice.
If the process is giving equal outputs to multiple candidates, then you might need to fix it to be more granular. There's plenty of room for subjective criteria, e.g. it shouldn't be hard to have a few ranks on "seems nice to work with" metric and so it shouldn't be hard to have a process where equal outputs are unlikely.
On the other hand there's the concept of a journeyman in the craft world, anyone with that level of skill is basically interchangeable for the usual jobs. So you might by design have a test that just outputs "at least journeyman level" or not, and you can just hire the first one you find.
Well, I suppose if we only look at first year college grad, there is a lot of duplicates.
From a hiring perspective, it cuts down the size of your candidate pool. Interviewing is expensive, so it’s better to choose from 5 people instead of 30.
From a personal perspective, I don’t want to work with you if you don’t know how to use a hashmap. There’s a difference between filtering duplicates efficiently and validating a binary tree, so it does it some injustice to lump it all together as irrelevant algorithms testing.