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Find Maximum Unique-Character Subset

Last updated: Apr 6, 2026

Quick Overview

This question evaluates algorithm design and combinatorial optimization skills, specifically the ability to model disjoint-character constraints, handle large input volumes, and produce a performant selection that maximizes distinct characters.

  • hard
  • Meta
  • Coding & Algorithms
  • Machine Learning Engineer

Find Maximum Unique-Character Subset

Company: Meta

Role: Machine Learning Engineer

Category: Coding & Algorithms

Difficulty: hard

Interview Round: Onsite

Given a list of words, choose a subset such that no character appears more than once across the entire chosen subset. Return one optimal subset, not just the score, where optimal means maximizing the total number of distinct characters covered by the chosen words. If multiple optimal subsets exist, return any of them. Additional requirements: - Words may be large in number, potentially thousands to tens of thousands. - A word that contains duplicate characters internally is invalid and can be filtered out during preprocessing. - If multiple words map to the same character set, such as anagrams, you may keep only one representative. - A naive backtracking solution is too slow on large test files, so the implementation should be optimized and still be able to reconstruct the selected subset.

Quick Answer: This question evaluates algorithm design and combinatorial optimization skills, specifically the ability to model disjoint-character constraints, handle large input volumes, and produce a performant selection that maximizes distinct characters.

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Meta
Mar 1, 2026, 12:00 AM
Machine Learning Engineer
Onsite
Coding & Algorithms
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Given a list of words, choose a subset such that no character appears more than once across the entire chosen subset. Return one optimal subset, not just the score, where optimal means maximizing the total number of distinct characters covered by the chosen words. If multiple optimal subsets exist, return any of them.

Additional requirements:

  • Words may be large in number, potentially thousands to tens of thousands.
  • A word that contains duplicate characters internally is invalid and can be filtered out during preprocessing.
  • If multiple words map to the same character set, such as anagrams, you may keep only one representative.
  • A naive backtracking solution is too slow on large test files, so the implementation should be optimized and still be able to reconstruct the selected subset.

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