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Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Apr 2026

Summary

Prepped hard for Google but skipped tries, figuring it wasn't worth the time. Guess which topic came up.

Questions Asked (1)

Q1

Solve a problem involving a trie data structure.

Algorithms & Data Structures
Author's notes

I'd covered basically everything: dp, graphs, the works.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem requirements and constraints, then propose a trie-based solution, explaining its advantages over alternatives like hash maps. Walk through the implementation details, including node structure and operations, and analyze time and space complexity.

Pro tip: Demonstrate awareness of trade-offs: mention that tries excel for prefix-based operations but can have high memory overhead, and suggest optimizations like compressed tries or ternary search trees when appropriate.

1. Clarify the Problem

Ask questions to understand the exact requirements, such as the operations needed (insert, search, prefix search), input size, and constraints. Confirm whether the problem involves strings or other sequences.

2. Propose Trie Solution

Explain why a trie is suitable, highlighting efficient prefix matching and predictable O(L) operations. Compare with alternatives like hash maps or binary search trees to justify the choice.

3. Design the Trie

Describe the node structure (e.g., children array/map, isEndOfWord flag) and outline key operations: insert, search, and startsWith. Discuss handling of edge cases like empty strings or duplicate insertions.

4. Analyze Complexity

Provide time and space complexity for each operation, where L is the length of the word. Mention that space can be high due to pointers, and discuss potential optimizations.

5. Test and Optimize

Walk through a small example to verify correctness. Discuss possible optimizations like using a hash map for children to save space or a compressed trie for long strings.

Key Points to Mention

  • Trie node structure: typically an array of size 26 for lowercase English letters or a hash map for arbitrary characters, plus a boolean flag for end of word.
  • Time complexity: O(L) for insert, search, and prefix search, where L is the length of the string.
  • Space complexity: O(N * L * alphabet_size) in the worst case, but can be optimized with sharing.
  • Use cases: autocomplete, spell checkers, IP routing (longest prefix match), and word games.
  • Comparison with hash tables: tries provide ordered traversal and prefix queries, but may use more memory.
  • Optimization techniques: compressed tries (radix trees), ternary search trees, and using arrays vs. hash maps based on alphabet size.

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