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Two passes: count frequencies first, then scan again for the first count-of-one character.
Start by clarifying the problem: define 'first' as the earliest index in the original string, and confirm whether the string contains only ASCII or Unicode characters. Then propose a two-pass solution using a hash map to count frequencies, followed by a second pass to find the first character with count 1. Discuss trade-offs between time and space, and mention edge cases like empty string or all repeating characters.
Pro tip: In data science roles, interviewers value practical optimization: mention that for large strings, you can use a fixed-size array (e.g., 256 for ASCII) instead of a hash map to reduce overhead, and that this approach is O(n) time and O(1) space. Also, proactively discuss how you would test the solution with edge cases and measure performance.
Ask about the character set (ASCII vs Unicode), string length, and whether the string can be empty. Confirm that 'first' means the earliest index in the original string.
Decide between a hash map (general) or fixed-size array (ASCII) for frequency counting. Explain that a two-pass approach is optimal: first pass to count, second pass to find the first unique.
Describe step-by-step: iterate through the string to populate the frequency map, then iterate again to return the index of the first character with count 1. If none, return -1.
State time complexity O(n) and space complexity O(k) where k is the number of unique characters (or O(1) for fixed alphabet). Discuss alternatives like using an ordered dictionary or linked list for single-pass, but note they add complexity.
Mention edge cases: empty string, all repeating characters, single character, and strings with special characters. Suggest writing unit tests to verify correctness.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.