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Uber·Software Engineer·Technical Phone Screen·Senior

Senior
Jun 2026

Summary

Uber SWE interview with a tree-based palindrome pairs problem. The algorithmic depth here was real, not just a standard leetcode warmup. Came away thinking I should practice iterative DFS more because the call-stack constraint was the part that nearly tripped me up.

Questions Asked (1)

Q1

Given a tree where each node holds a single lowercase character, count the number of node pairs whose path characters can be rearranged into a palindrome. N is large enough that a naive recursive DFS will blow the call stack.

Algorithms & Data StructuresTechnical Trade-offs
Author's notes

The palindrome condition clicked pretty fast for me, at most one character with odd frequency, so you can encode a path as a 26-bit XOR mask.

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AI HintsAI Generated

Suggested Approach

Use an iterative post-order traversal to avoid stack overflow, and for each node, maintain a bitmask of character parities along the path from the root. Count valid pairs by checking if the XOR of two path masks has at most one bit set, using a hash map to track frequencies of masks in the subtree.

Pro tip: Mention that the bitmask approach reduces the palindrome condition to a simple XOR check, and that using DSU on tree (small-to-large merging) can achieve O(N log N) time, which is crucial for large N.

1. Clarify and Define

Confirm that a path between two nodes is the unique simple path, and that rearranging characters into a palindrome requires at most one character with odd frequency. Define the parity mask: a 26-bit integer where each bit indicates odd count of a character.

2. Iterative Traversal

Implement an iterative post-order DFS using an explicit stack to compute the parity mask from root to each node without recursion, avoiding call stack overflow.

3. Count Pairs Efficiently

For each node, count pairs with nodes in its subtree where the XOR of their path masks has at most one bit set. Use a hash map to store mask frequencies and merge subtrees using small-to-large merging (DSU on tree) to keep time O(N log N).

4. Handle Edge Cases

Consider pairs where one node is an ancestor of the other (including the root), and ensure the algorithm counts each pair exactly once. Also handle N=1 and large N gracefully.

5. Analyze Complexity

Explain that the time complexity is O(26 * N log N) due to bitmask checks and merging, and space complexity is O(N) for the hash map and stack. Emphasize that this avoids recursion depth issues.

Key Points to Mention

  • Palindrome condition: at most one character has odd frequency in the path.
  • Bitmask representation: 26-bit integer for parity of each character.
  • XOR of two path masks gives parity of characters on the path between nodes.
  • Iterative DFS to prevent stack overflow for large N.
  • Small-to-large merging (DSU on tree) for efficient subtree counting.
  • Time complexity O(N log N) and space O(N).

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