Kth ancestor in tree
You are given a rooted tree of n nodes labeled 0..n-1 as a parent array (parent[root] = -1). Answer a batch of kth-ancestor queries: for [node, k] return the label of the ancestor reached by going up k edges, or -1 if it does not exist (you walk past the root). Precompute jump pointers (up[j][v] = the 2^j-th ancestor) so each query decomposes k into its binary bits and resolves in O(log n). Return the list of answers in query order.
Implement
kth_ancestor(n: int, parent: list[int], queries: list[list[int]]) → list[int]Examples
in
[7,[-1,0,0,1,1,2,2],[[3,1],[3,2],[5,2],[3,5]]]out[1,0,0,-1]What a strong answer looks like
State your approach and its time/space complexity out loud before you optimize. Handle the edge cases (empty input, duplicates, overflow), and say why you chose this over the brute force. Green tests are the floor, not the grade.
0:00 of about 30 min
solution.py
InputExpectedGot
[7,[-1,0,0,1,1,2,2],[[3,1],[3,2],[5,2],[3,5]]][1,0,0,-1]not run yetsample