Google·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Google SWE interview with a pretty gnarly string problem that mixed combinatorics with dictionary lookups. The question had a lot of moving parts and I'm still not sure I nailed the complexity analysis.

Questions Asked (1)

Given a string and a dictionary of valid English words, return true if every subsequence of length 3 or more can be rearranged into some valid English word. Walk through your approach, including how you'd enumerate subsequences and check anagram validity, and analyze the time and space complexity.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The anagram check part felt manageable since character frequency maps are pretty standard.

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AI HintsAI Generated

Suggested Approach

First, clarify the problem: for every subsequence of length >=3, its characters can be rearranged into a valid English word. Then, propose an efficient solution by reducing the problem to checking if every multiset of characters of size >=3 that appears as a subsequence is an anagram of some dictionary word. Use a trie or hash map of sorted words for anagram lookup, and enumerate subsequences via recursion or bitmask, but optimize by pruning based on character counts.

Pro tip: Mention that the naive approach is exponential, so you'd optimize by grouping dictionary words by their sorted character signature and checking only subsequences that are minimal (e.g., length 3) because if all length-3 subsequences are valid anagrams, longer ones might not automatically be, but you can argue about monotonicity or use a counterexample to show the need for checking all lengths.

1. Clarify the problem and constraints

Restate the problem to ensure understanding: given a string s and a dictionary D, return true if every subsequence of s of length >=3 can be rearranged into a word in D. Ask about input sizes, dictionary size, and whether words can be reused.

2. Preprocess the dictionary

Create a hash set or trie of sorted words (anagram signatures) from the dictionary to allow O(1) or O(L) anagram checks. For each word, sort its characters and store in a set.

3. Enumerate subsequences efficiently

Use recursion or iterative bitmask to generate all subsequences of length >=3. Prune branches early if the current character multiset cannot possibly form a valid anagram (e.g., if its sorted signature is not a prefix of any dictionary anagram).

4. Check anagram validity

For each subsequence, sort its characters and check if the sorted string exists in the preprocessed set. If any subsequence fails, return false; if all pass, return true.

5. Analyze time and space complexity

Time: O(2^n * L log L) in the worst case, where n is string length and L is max subsequence length, but with pruning it can be much better. Space: O(2^n) for storing subsequences if not careful, but can be O(n) with backtracking; dictionary storage O(total characters in dictionary).

Key Points to Mention

The problem requires checking all subsequences, not substrings, so order matters for enumeration but not for anagram validity.
Anagram checking can be optimized by sorting characters or using character count arrays.
Pruning is crucial: if a subsequence's character counts exceed the maximum counts in any dictionary word, it can be skipped.
There is a potential optimization: if all length-3 subsequences are valid, longer ones might not be, so you must check all lengths; but you can use the fact that if a longer subsequence fails, it contains a failing shorter subsequence? Not necessarily, so careful.
Discuss trade-offs between precomputing all anagrams vs. on-the-fly checking.
Mention that the problem is likely NP-hard in general, so interviewers may expect a discussion of heuristics or constraints.

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