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Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
May 2026

Summary

Google software engineer interview with a classic data structures question. Nothing too surprising but the implementation details are where it gets you.

Questions Asked (1)

Q1

Implement a Trie (prefix tree) data structure with insert, search, and startsWith methods.

Algorithms & Data Structures
Author's notes

Knew what a trie was, drew it out fine, but my initial node class was a mess.

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AI HintsAI Generated

Suggested Approach

Clarify the requirements and constraints, then design a TrieNode class with children and isEndOfWord, and implement the three methods using iterative traversal. Discuss time and space complexity, and consider edge cases like empty strings and special characters.

Pro tip: Mention that tries are ideal for prefix-based operations and can be optimized with arrays or hash maps for children, and discuss real-world applications like autocomplete and spell checkers to show depth.

1. Clarify Requirements

Ask about character set (e.g., lowercase a-z), maximum word length, and whether empty strings are allowed. Confirm that search requires exact match and startsWith only needs prefix.

2. Design Data Structure

Define a TrieNode with a children collection (e.g., array of size 26 or hash map) and a boolean isEndOfWord. The Trie class holds a root node.

3. Implement Insert

Iterate through each character of the word, creating nodes as needed, and mark the final node's isEndOfWord as true.

4. Implement Search and StartsWith

For both, traverse the trie following the characters. For search, return true only if traversal succeeds and the final node's isEndOfWord is true; for startsWith, return true if traversal succeeds.

5. Analyze Complexity and Edge Cases

State that time complexity is O(m) for all operations where m is word length, and space is O(total characters). Discuss edge cases like empty string, single character, and non-alphabetic characters.

Key Points to Mention

  • Time complexity: O(m) for insert, search, and startsWith, where m is the length of the word/prefix.
  • Space complexity: O(n * m) worst case, but often less due to shared prefixes.
  • Choice of children data structure: array for fixed alphabet (faster, more memory) vs hash map for dynamic alphabet (flexible, less memory).
  • Use of isEndOfWord flag to distinguish complete words from prefixes.
  • Handling of edge cases: empty string, null input, and characters outside expected set.
  • Real-world applications: autocomplete, spell check, IP routing (trie).

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