Intuit·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

May 2026

Summary

Intuit software engineer interview, technical phone screen focused on tree traversal with a custom query system. Pretty interesting problem actually, not your typical LeetCode grind.

Questions Asked (1)

You're given an array encoding a rooted tree where each element stores the parent of that node. You receive multiple queries, each starting at some node and executing a sequence of moves (go to parent, or go to k-th child by sorted order). Return the final node after all moves. How do you preprocess the tree and handle each query efficiently?

Algorithms & Data StructuresSystem Design

Author's notes

The encoding tripped me up for a second because it's 1-indexed parents stored at 0-indexed positions, and I kept confusing myself on the offset.

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AI HintsAI Generated

Suggested Approach

Preprocess the tree by building an adjacency list with children sorted by node value, and compute binary lifting tables for both parent and child jumps. For each query, simulate the moves efficiently using the precomputed tables to jump multiple steps at once, ensuring O(log N) per move.

Pro tip: Clarify the constraints upfront (e.g., number of nodes, queries, and k values) to choose the right preprocessing depth; often, a simpler approach like storing children in sorted order and using binary lifting for parents suffices, but if k-th child queries are frequent, consider a more advanced structure like a persistent segment tree or wavelet tree.

1. Understand the problem and constraints

Clarify the input format: parent array, query format (start node and sequence of moves), and constraints on N, Q, and k. Determine if moves are given as strings or integers and if k-th child is 1-indexed.

2. Preprocess the tree structure

Build an adjacency list where each node's children are sorted by their node values. Compute depth and parent pointers for each node, and set up binary lifting tables for O(log N) ancestor queries.

3. Handle k-th child queries efficiently

For each node, store its sorted children list. To find the k-th child, use binary search or direct indexing if k is small; if k can be large, consider augmenting with a data structure like a Fenwick tree per node or a persistent segment tree over the Euler tour.

4. Process each query

For each move, if it's 'go to parent', use binary lifting to jump up one level in O(1) or O(log N). If it's 'go to k-th child', retrieve the k-th child from the sorted list in O(log degree) or O(1) if using an array. Continue until all moves are processed.

5. Analyze time and space complexity

Preprocessing takes O(N log N) time and space for binary lifting and sorting children. Each query takes O(M log N) where M is the number of moves, assuming O(log N) per move. Discuss potential optimizations if needed.

Key Points to Mention

Building sorted children lists for each node to support k-th child queries.
Binary lifting (or jump pointers) for efficient parent traversal.
Handling large k values with binary search or advanced data structures.
Time complexity: O(N log N) preprocessing, O(M log N) per query.
Space complexity: O(N log N) for binary lifting tables.
Edge cases: root node has no parent, k exceeding number of children, invalid moves.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.