Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jul 2026

Summary

Google SWE interview with a string problem that looks simple until you actually think through what's being asked. One question, but it had some teeth.

Questions Asked (1)

Given a string of letters and access to a dictionary of valid English words, return true if every contiguous substring of length 3 or more is a valid dictionary word. Otherwise return false.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was to just iterate through all pairs and check each substring, which is technically correct but I fumbled the complexity analysis when they pushed back.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem constraints and edge cases, such as string length and dictionary size. Then, propose an efficient algorithm that checks all substrings of length >=3, possibly using a trie or set for O(1) lookups, and analyze its time and space complexity. Finally, discuss potential optimizations or alternative approaches, considering trade-offs.

Pro tip: Demonstrate awareness of the brute-force O(n^3) approach and then optimize by noting that if any substring of length k is invalid, all longer substrings containing it are also invalid, allowing early termination. This shows you think about pruning and efficiency.

1. Clarify requirements and constraints

Ask about input size limits, dictionary size, and whether the string can be empty or contain non-letter characters. Confirm that 'contiguous substring' means any substring, not just those starting at certain positions.

2. Outline a brute-force approach

Describe checking every substring of length >=3 by generating all O(n^2) substrings and verifying each against the dictionary, leading to O(n^3) time if using naive string comparison. This establishes a baseline.

3. Optimize with efficient data structures

Propose using a hash set or trie for O(1) or O(L) dictionary lookups (L = word length). Then, iterate over all start positions and lengths, but note that if a substring of length k is invalid, any longer substring containing it is also invalid, so we can break early.

4. Analyze complexity and trade-offs

Calculate time complexity: worst-case O(n^3) if no early termination, but with pruning it can be much better. Space complexity O(D) for dictionary storage. Discuss trade-offs between preprocessing the dictionary (e.g., building a trie) and on-the-fly lookups.

5. Test with examples and edge cases

Walk through simple examples like 'abc' with dictionary containing 'abc', and edge cases like empty string, string length <3, and strings with repeated characters. Verify the algorithm returns correct results.

Key Points to Mention

Time and space complexity analysis, including best and worst cases.
Use of appropriate data structures (hash set, trie) for dictionary lookups.
Early termination optimization: if a substring of length k is invalid, longer substrings containing it are also invalid.
Handling edge cases: empty string, string length <3, and dictionary containing all possible substrings.
Trade-offs between preprocessing the dictionary vs. on-the-fly checking.
Potential for further optimization using dynamic programming or suffix automata, if applicable.

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