The path goes leaf-to-root but the string reads that direction, so you have to think about it carefully or you'll build the string backwards and not realize it until you're comparing.
Perform a depth-first search (DFS) from the root, maintaining the current path string. At each leaf, reverse the path to get the leaf-to-root string and compare it with the current best. Use pruning: if the current path prefix is already lexicographically larger than the corresponding prefix of the best string, backtrack early.
Pro tip: In a Meta ML Engineer interview, emphasize that this problem tests tree traversal and string comparison, but also highlight how you would optimize for large trees using pruning and possibly iterative DFS to avoid recursion limits. Mention that the same pattern applies to sequence modeling in ML, such as beam search.
Confirm that the path must start at a leaf and end at the root, and that the string is formed by concatenating node values mapped to letters. Discuss edge cases: empty tree, single node, and multiple leaves.
Decide between DFS (recursive or iterative) and BFS. DFS is natural because we need to explore complete paths. Explain that you will maintain the current path and update the best string when reaching a leaf.
During DFS, build the path string. At each node, compare the reversed current path with the best string found so far; if it's already lexicographically larger, prune the branch. At leaves, reverse the path and update the best if smaller.
State that the worst-case time complexity is O(N * L) where N is the number of nodes and L is the maximum path length, due to string comparisons. Space complexity is O(H) for recursion stack, where H is the tree height.
Walk through a small example to verify correctness. Discuss potential optimizations: using a list of characters instead of string concatenation, or comparing character by character without building full strings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.