Without getting too pedantic, it can be difficult to define what encompasses "expertise". You could have worked on some backend payment system for years and conclude that you're a payment "expert". But that means different things to different people.
Not all current "experts" are necessarily hired into corresponding roles needing the same expertise. To put it more concretely with an example, it's entirely possible for you to be a payment system expert but have a recruiter reach out to you for a general SWE role (you're welcome to decline). The slate of roles that do require said expertise are limited. If there's 10 qualified candidates, they could all technically pass the hiring bar. But if there's only 7 positions that require that exact expertise, then what happens to the other 3? Well, they're broadcast to "matching" teams that have available headcount. This "match" could be approximate, but it underscores why getting a signal beyond just the domain expertise is important.
We could of course debate whether something "advanced" should be asked, but that would require us to agree on what constitutes "advanced". In general, it's difficult to pin point some threshold and unanimously agree that that's the level of difficulty of generalist SWE questions that a domain expert should get asked. For what it's worth, there's additional intervention by committee(s) that look at this on a case-by-case basis to make sure that everything's on an even keel. Concretely, if you're an ML expert
interviewing for a role that specifically demands ML-expertise and you were asked some advanced question about data structures (ex: something needing the Hungarian algorithm) and you're upset that you bombed it, the don't be; the committee(s) would weigh that interview's score less if they expect something alone the lines of Maps/Queues to be asked instead. These are all made up examples, but I hope that it conveys why there's a need to extract signal beyond just core-expertise. Google doesn't just artificially create hurdles for sadistic pleasure ¯\_(ツ)_/¯
Not all current "experts" are necessarily hired into corresponding roles needing the same expertise. To put it more concretely with an example, it's entirely possible for you to be a payment system expert but have a recruiter reach out to you for a general SWE role (you're welcome to decline). The slate of roles that do require said expertise are limited. If there's 10 qualified candidates, they could all technically pass the hiring bar. But if there's only 7 positions that require that exact expertise, then what happens to the other 3? Well, they're broadcast to "matching" teams that have available headcount. This "match" could be approximate, but it underscores why getting a signal beyond just the domain expertise is important.
We could of course debate whether something "advanced" should be asked, but that would require us to agree on what constitutes "advanced". In general, it's difficult to pin point some threshold and unanimously agree that that's the level of difficulty of generalist SWE questions that a domain expert should get asked. For what it's worth, there's additional intervention by committee(s) that look at this on a case-by-case basis to make sure that everything's on an even keel. Concretely, if you're an ML expert interviewing for a role that specifically demands ML-expertise and you were asked some advanced question about data structures (ex: something needing the Hungarian algorithm) and you're upset that you bombed it, the don't be; the committee(s) would weigh that interview's score less if they expect something alone the lines of Maps/Queues to be asked instead. These are all made up examples, but I hope that it conveys why there's a need to extract signal beyond just core-expertise. Google doesn't just artificially create hurdles for sadistic pleasure ¯\_(ツ)_/¯