So, for instance, you could give a data science applicant a dataset and pandas and tell them to make you a report 'about sales projections' then see what they come back with.
Or you could ask them to sort a list of strings in log(n) time and n space using a recursive solution.
The first test would probably benifit an experieced applicant because hopefully they will have a better understanding of what buisness actually needs. The second test would benifit a recent graduate, because they will have done algorithms 101 more recently and remember a canned solution.
Of course, experienced applicants will revise this sort of problem and play on hacker rank for a couple months before a job hunt, so it will also benifit candidates who are actively job hunting, rather than applying for a specific oppertunity
A whiteboard interviewer typically finds some type of "library" problem, gives it a little twist and throws it at a senior programmer who's been doing design and structure for years. The candidate out of school, meanwhile, recently took an algorithms course.
So for the older candidate to keep up with the younger one the older one actually has to go and study stuff UNRELATED to work.
I find it strange that you can be older than the Parent poster but not realize this fact.
If the GP has had some amount of luck and/or skill with picking the places they interview at, they might not have experienced this too much.
Reading between the lines, you seem upset about having to answer questions about technical problems you perceive to be irrelevant, and that these technical problems are more likely to be solved successfully by those who have recently practiced them (e.g. graduates).
I too agree that, while abstract technical interview questions have little bearing on most day-to-day work, they do some things quite well:
1 - Define a well-bounded problem of sufficient difficulty 2 - Give the interviewee a good baseline for objective success 3 - Exercise a person's mind to think about complicated problems
While they are contrived and not entirely representative of daily work, they are not without value.
This might be fine if the "very similar problem" is something you'd likely have encountered in your work, but often they questions are drawn from a pool that most people in this line of work see rarely if at all, and if they do it's likely to be some very small subset of the questions, so dedicated study of the remainder of the pool still puts one at a large advantage, regardless how useful it is in doing your actual work.
They're measures of "how bad you want it" (how much of your time you spent memorizing stuff you don't actually use to prep for the interviews) and/or how recently you took an algorithms course. And maybe those are things worth measuring, I dunno. Maybe the absence of strong enough signals on either of those is important enough that it makes sense to use them to reject people who are otherwise very capable of doing the actual work.
In much of science the hard part is asking the right question rather than coming up with the solution, so figuring out these algorithms is not equivalent to making publishable research.
What they aren't the best at, although more realistic, is coming up with an objective measurement of quality. Algorithms , at the cost of realism, do measure things quite nicely.
That gives you the interview and determines what level you get if you get hired, so it still matters more than the white-board. White-boarding is still necessary to check that you are top x% in terms or some mishmash of skill and intelligence. Having tech leads who are less talented than our juniors would be bad, so testing that they are better in every way is important.
I do sympathize though. The older I get, the slower I tend to think and the more silly mistakes I make. WBC is not a good way to show off my skills. Frankly I think WBC in general is biased towards the type of rote learners who grind through leetcode problems and know how to regurgitate canned answers, but that is dependent on your interviewer.
See - I've even gone through that. I'm that diligent.