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Trexquant·Software Engineer·Technical Phone Screen·Intermediate

Intermediate
May 2026

Summary

Trexquant SWE interview had me implementing a Trie-based dictionary in C++ with wildcard search support. Pretty focused technical round, no fluff.

Questions Asked (1)

Q1

Design a Trie-based text dictionary in C++ that supports inserting words and searching for patterns where a '.' character can match any single lowercase letter.

Algorithms & Data StructuresSystem Design
Author's notes

The insert part was fine, standard Trie node with 26 children.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: the dictionary supports inserting words and searching patterns where '.' matches any single lowercase letter. Then design a Trie where each node has an array of 26 children and a boolean flag for end-of-word, and implement search recursively: for a regular character, follow the corresponding child; for '.', recursively try all non-null children. Finally, discuss time and space complexity and potential optimizations like memory pooling or compressed tries.

Pro tip: Mention that the recursive search for '.' can be optimized by pruning branches early and that you can use a vector of child pointers instead of a fixed array to save memory for sparse nodes. Also, clarify that the solution should handle edge cases like empty strings and patterns with consecutive dots.

1. Clarify requirements and constraints

Confirm that words and patterns consist only of lowercase letters and '.', and that '.' matches exactly one character. Ask about expected word length, number of operations, and memory constraints.

2. Design the Trie data structure

Define a TrieNode with an array of 26 pointers (or a hash map) for children and a boolean isEndOfWord. Explain that this structure allows efficient prefix-based operations.

3. Implement insertion

Traverse the Trie for each character in the word, creating nodes as needed, and mark the last node as end-of-word. This is O(L) time where L is word length.

4. Implement pattern search with '.' wildcard

Use a recursive helper function that takes a node and the pattern index. If the current pattern character is '.', recursively search all non-null children; otherwise, follow the specific child. Return true if the pattern is fully consumed and the node is end-of-word.

5. Analyze complexity and discuss optimizations

State that search time is O(26^d * L) in the worst case where d is the number of dots, but typically much faster. Mention space optimizations like using a map for children or a compressed trie (radix tree) if memory is a concern.

Key Points to Mention

  • Trie node structure with children array and end-of-word flag
  • Recursive backtracking for '.' wildcard matching
  • Time complexity: O(L) for insert, O(26^d * L) worst-case for search with d dots
  • Space complexity: O(N * L * 26) for N words of average length L, but can be optimized
  • Handling edge cases: empty word, empty pattern, pattern with only dots
  • Potential optimizations: memory pooling, compressed trie, or using a hash map for children

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