Ziphq·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Interviewed for a software engineering role at Ziphq and got hit with a string/phrase coverage problem that looked approachable until I actually tried to think through the general case. The algorithmic depth required was more than I expected for what seemed like a warmup problem.

Questions Asked (1)

Given a list of phrases, find the contiguous sub-phrase (spanning any number of words, not necessarily a full string) that maximizes coverage, where coverage is defined as the word count of the sub-phrase multiplied by the number of input strings that contain it as a contiguous word-boundary match.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

My first instinct was a trie and I ran with it way too long.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints and edge cases, then propose a solution that enumerates all possible contiguous sub-phrases (n-grams) across the input strings. For each candidate, compute its coverage by counting the number of strings where it appears as a contiguous word-boundary match, and track the maximum. Discuss trade-offs between brute-force and optimized approaches using suffix automata or inverted indices.

Pro tip: Mention that the optimal sub-phrase is likely short (1-3 words) because coverage drops exponentially as phrase length increases, so you can limit the maximum n-gram length to keep the solution efficient. Also, highlight that word-boundary matching requires careful tokenization to avoid partial word matches.

1. Clarify requirements and constraints

Ask about input size, expected phrase lengths, and whether case sensitivity or punctuation matters. Confirm that coverage is computed as word count times the number of strings containing the sub-phrase as a contiguous sequence of whole words.

2. Enumerate candidate sub-phrases

Generate all contiguous n-grams (n from 1 to max possible) from each string, ensuring they respect word boundaries. Use a set to deduplicate candidates across strings.

3. Compute coverage efficiently

For each candidate, count how many strings contain it as a contiguous word-boundary match. Use a hash map to store candidate phrases and their counts, or build an inverted index from n-grams to string IDs.

4. Track and return the maximum

Iterate through candidates, compute coverage as word count times frequency, and keep the candidate with the highest coverage. Handle ties by returning any or the shortest phrase.

5. Discuss optimizations and trade-offs

Propose pruning: limit n-gram length based on coverage upper bound (e.g., if max possible coverage for longer phrases is less than current best). Mention advanced data structures like suffix automata or Aho-Corasick for large inputs.

Key Points to Mention

Word-boundary matching: ensure sub-phrases are sequences of whole words, not substrings within words.
Coverage formula: word count of sub-phrase multiplied by number of strings containing it.
Brute-force complexity: O(N * L^2) where N is number of strings and L is average length, but can be optimized.
Pruning strategy: limit maximum n-gram length because longer phrases have lower frequency and thus lower coverage.
Data structures: use hash maps for frequency counting, or suffix automata for efficient substring queries.
Edge cases: empty input, single-word strings, phrases with punctuation, and ties in coverage.

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