I am not so sure about this technique being specific. I'd say sparse tables are quite well known and presented to students at one of Algorithms and Data Structu

I am not so sure about this technique being specific. I'd say sparse tables are quite well known and presented to students at one of Algorithms and Data Structures courses at most universities. Another generally known solution applicable here would be segment trees with Theta(n) preprocessing and Theta(log n) query. Also the problem of querying range sums, as the author himself notes, has actually much simpler solution in form of a prefix sum array, which is exactly what the name suggests and can be computed in one iteration, queries being constant time. Obviously self-taught developers might be unfamiliar with these concepts and one might make a point of these questions screening for computer science background, but I personally believe that being aware of fundamental algorithm design techniques is important for programmers working on non-trivial problems. Some people say that you can always google these things, but can you really? It's like saying that you don't have to learn integration and just continue on with calculus, always being able to just look up needed formulas as you go. The value of these fundamental algorithms is much more than just knowing how to write them when the need arises. They teach you general principles of algorithm design, present with many patters you can apply in problem solving and create a solid foundation for being able to deal with non-trivial problems. Knowing how segment trees or sparse tables work opens you up to whole new domains of useful applications building on them. There are reasons why these are generally taught at undergraduate level.