Meta·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

May 2026

Summary

Meta SWE coding round with a bitmask subset problem. Pretty clean problem once you see the trick, but I spent way too long second-guessing the preprocessing step.

Questions Asked (1)

Given an array of strings, find the maximum length of a concatenated string formed by choosing a subset such that every character in the result appears exactly once.

Algorithms & Data Structures

Author's notes

The bitmask approach clicked for me pretty fast since each string maps cleanly to a 26-bit integer.

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AI HintsAI Generated

Suggested Approach

Use bitmasking to represent the set of characters in each string, prefiltering strings with duplicate characters. Then use dynamic programming or backtracking to explore subsets, tracking used characters via bitmask and maximizing total length.

Pro tip: Clarify constraints upfront (e.g., alphabet size, string lengths) to choose the right approach; mention that bitmasking is efficient for small alphabets (like 26 lowercase letters) and that pruning invalid strings early saves time.

1. Understand the problem and constraints

Restate the problem: choose a subset of strings such that concatenation has all unique characters, maximize length. Ask about alphabet size, string lengths, and input size to guide algorithm choice.

2. Preprocess strings with bitmasks

For each string, compute a bitmask of its characters. Discard any string with duplicate characters (mask has fewer bits than string length).

3. Explore subsets with DP or backtracking

Use dynamic programming over bitmask states (dp[mask] = max length) or backtracking to try including/excluding each string, ensuring no character overlap (mask & newMask == 0).

4. Optimize and handle edge cases

Consider pruning (e.g., skip strings that are subsets of others), and handle empty input or strings with no valid subset. Discuss time/space complexity.

5. Test with examples and discuss trade-offs

Walk through a small example, verify correctness, and compare approaches (e.g., DP vs. backtracking) in terms of time and space.

Key Points to Mention

Bitmask representation of character sets for O(1) overlap checks.
Preprocessing to remove strings with duplicate characters.
Dynamic programming state: dp[mask] = maximum length achievable with used characters mask.
Backtracking with pruning as an alternative for smaller inputs.
Time complexity: O(2^A * N) where A is alphabet size and N is number of strings, or O(2^N) for backtracking.
Space complexity: O(2^A) for DP table or O(N) for backtracking recursion.

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