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Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say
Jun 2026

Summary

Meta SWE coding round, one question on prefix finding. Pretty algorithmic, they pushed for optimal complexity which is where it got interesting.

Questions Asked (1)

Q1

Given a list of strings, find the shortest unique prefix for each string and return them as a list.

Algorithms & Data Structures
Author's notes

My first instinct was brute force, comparing each string against all others character by character.

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AI HintsAI Generated

Suggested Approach

Build a trie from all strings, then for each string traverse the trie until reaching a node with exactly one child (or the string's end), which marks the shortest unique prefix. Alternatively, sort the strings and compare adjacent strings to find the minimal distinguishing prefix for each. Discuss trade-offs between trie (O(total characters) time and space) and sorting (O(n log n * L) time, O(1) extra space).

Pro tip: Clarify edge cases upfront: duplicate strings, empty strings, and strings where one is a prefix of another. Mention that if duplicates exist, no unique prefix is possible, so you must handle that explicitly.

1. Clarify requirements and edge cases

Ask about input constraints (string length, number of strings, character set) and edge cases like duplicates, empty strings, and prefix relationships. Confirm expected output for duplicates.

2. Choose data structure and algorithm

Decide between a trie and sorting. Explain that a trie naturally groups strings by common prefixes and allows efficient prefix extraction, while sorting leverages lexicographic order to find minimal distinguishing prefixes.

3. Outline the algorithm step-by-step

For trie: insert all strings, then for each string traverse until the current node has only one child or the string ends. For sorting: sort strings, then for each string compare with its neighbors to find the shortest prefix that differs.

4. Analyze complexity and trade-offs

State time and space complexity for both approaches. Trie: O(N * L) time and space, where N is number of strings and L is average length. Sorting: O(N log N * L) time, O(1) extra space (if in-place).

5. Handle edge cases and test

Walk through examples including duplicates, empty strings, and one string being a prefix of another. Verify that the algorithm returns correct unique prefixes or handles impossible cases.

Key Points to Mention

  • Trie construction and traversal to find the first node with a single child
  • Sorting approach: comparing adjacent strings to find minimal distinguishing prefix
  • Time and space complexity analysis for both approaches
  • Handling duplicates: if a string appears more than once, no unique prefix exists
  • Edge cases: empty strings, single-character strings, and strings that are prefixes of others
  • Trade-offs: trie uses more memory but is straightforward; sorting is simpler but may be slower for large N

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