Spent the first few minutes just making sure I understood what 'no duplicate characters' meant for individual words versus across the subset.
Use bitmask representation to encode each word's unique character set as an integer, then apply a greedy or backtracking search to find the subset of non-overlapping bitmasks whose combined bit count is maximized. This approach leverages bitwise AND to quickly check for character conflicts and bitwise OR to accumulate the union of characters. Start by filtering out words with duplicate characters, since they can never contribute to a valid subset.
Pro tip: Mention that pre-filtering words with duplicate characters and deduplicating bitmasks (multiple words can share the same character set) significantly prunes the search space — this shows awareness of practical optimization beyond the naive solution, which Meta interviewers value highly.
Confirm that 'no word contains duplicate characters' means each word in the chosen subset must itself have all unique letters, and that no two words in the subset share any character. Ask about constraints like word length, alphabet size, and list size to gauge expected complexity.
Discard any word that contains a repeated character (detectable in O(n) per word). For each remaining word, encode its character set as a 26-bit integer where bit i is set if the i-th letter of the alphabet is present, enabling O(1) conflict checks via bitwise AND.
Multiple words may map to the same bitmask; keep only unique bitmasks since choosing any one representative is equivalent. This reduces the effective search space and avoids redundant computation during the subset search.
Use a recursive backtracking approach: iterate over bitmasks, and for each candidate, check if it conflicts with the current accumulated bitmask (AND == 0). If not, OR it in and recurse, tracking the maximum popcount (total unique characters) seen across all valid subsets.
The worst-case is O(2^N) over filtered bitmasks, but in practice the alphabet constraint (26 bits) and early pruning make it tractable. Discuss potential optimizations such as sorting bitmasks by popcount descending for better pruning, or using memoization on the accumulated bitmask state.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.