Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

Google SWE interview with a tricky string/dictionary problem that pushed into backtracking territory pretty fast. The complexity analysis part was where things got uncomfortable.

Questions Asked (1)

Given a string and a dictionary of valid English words, return true if every subsequence of the string with length 3 or more is a valid English word. Walk through a recursive or backtracking solution and analyze the time and space complexity.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The problem statement itself took me a minute to parse.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem: a subsequence is any sequence obtained by deleting characters without reordering, so we need to check all subsequences of length ≥3. Then, design a recursive backtracking solution that generates each subsequence and checks it against the dictionary, pruning early if an invalid word is found. Finally, analyze the time complexity as O(2^n * L) where L is the average word length, and space complexity as O(n) for recursion depth, noting that this is exponential and likely impractical for large n.

Pro tip: Mention that this problem is likely a trick or stress-test question: in practice, you'd never check all subsequences due to exponential blowup, and you should discuss optimizations like memoization or early termination, or question the problem's feasibility.

1. Clarify the problem

Confirm that 'subsequence' means any sequence obtained by deleting characters without reordering, and that we need to check all subsequences of length 3 or more. Ask about constraints on string length and dictionary size.

2. Design recursive backtracking

Use a recursive function that builds subsequences by including or excluding each character. When the current subsequence length reaches 3, check if it's in the dictionary; if not, return false. Continue until all subsequences are checked.

3. Implement and prune

Implement the recursion with early termination: as soon as an invalid subsequence is found, return false. Optionally, use memoization to avoid rechecking the same subsequence, though the number of subsequences is exponential.

4. Analyze complexity

Time complexity is O(2^n * L) where n is string length and L is average word length for dictionary lookup. Space complexity is O(n) for recursion stack, plus O(2^n) if storing all subsequences, but we can avoid that.

5. Discuss trade-offs and optimizations

Note that the brute-force approach is exponential and impractical for large n. Discuss possible optimizations like using a trie for the dictionary, or recognizing that the problem is likely NP-hard and may not have a polynomial solution.

Key Points to Mention

Definition of subsequence and why it's different from substring.
Recursive backtracking approach with include/exclude decisions.
Early termination when an invalid subsequence is found.
Time complexity O(2^n * L) and space complexity O(n) for recursion.
The exponential nature makes this impractical for large inputs; discuss optimizations or problem constraints.
Use of a trie or hash set for efficient dictionary lookups.

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