Interview Pro Tip
Red Flag: Using sorted(lst)[-1]—O(n log n) and unnecessary. Pro-Move: 'I handle empty input explicitly, use a single pass, and mention that for numeric arrays numpy.max is faster due to C implementation.'
**Logic**: Init with first element; iterate and update when current > max. **Code**: `def find_max(lst):\n if not lst: return None\n max_val = lst[0]\n for x in lst[1:]:\n if x > max_val: max_val = x\n return max_val`. **Complexity**: O(n) time, O(1) space. **Edge cases**: Empty list (return None or raise); all same values; single element. **Scalability**: Single pass; suitable for iterables (use iterator, not index, for large data)....
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