Trexquant·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Trexquant had me implement a Trie with wildcard search support. Pretty focused technical round, one meaty problem with follow-ups on complexity and recursion depth.

Questions Asked (1)

Build a Trie data structure that supports adding words and searching for them, where the search pattern can include '.' as a wildcard matching any single character. Walk through the time/space complexity and explain how you'd handle queries with many wildcards without letting the recursion blow up.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The basic Trie part was fine, I've done that before.

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AI HintsAI Generated

Suggested Approach

Start by defining the Trie node structure with children and an isEnd flag, then implement insertion and search with recursive DFS for wildcard matching. Analyze time and space complexity, and discuss optimizations like pruning and iterative approaches to handle many wildcards efficiently.

Pro tip: Mention that wildcard search can be optimized by storing word lengths or using a BFS with a queue to avoid recursion depth issues, and always consider the trade-off between recursion simplicity and iterative robustness.

1. Define Trie Node and Insertion

Describe the Trie node structure (e.g., array or hashmap of children, boolean isEnd) and implement insertion of a word in O(L) time.

2. Implement Search with Wildcards

Write a recursive search function that handles '.' by branching to all children, and exact characters by following the specific child.

3. Analyze Complexity

Explain that search time is O(L) for exact matches and O(26^L) worst-case for many wildcards, but typically much less; space is O(N*L) for N words of average length L.

4. Handle Many Wildcards

Discuss strategies to prevent recursion blow-up: pruning branches that exceed remaining pattern length, using iterative BFS with a queue, or memoization of visited states.

5. Discuss Trade-offs and Optimizations

Mention trade-offs between recursion and iteration, memory vs. speed, and potential optimizations like storing word lengths or using a trie with compressed nodes.

Key Points to Mention

Trie node structure with children and isEnd flag
Recursive DFS for wildcard matching with branching on '.'
Time complexity: O(L) for exact, O(26^L) worst-case for wildcards
Space complexity: O(N*L) for storage, O(L) for recursion stack
Pruning: stop recursion if remaining pattern length exceeds remaining trie depth
Iterative BFS with queue to avoid recursion depth limits

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