The whole-word matching piece is where I almost tripped up.
Clarify the input structure and define 'whole word' matching, emphasizing that punctuation should be treated as word boundaries. Then propose a solution that normalizes each message by extracting words using a regex or splitting on non-alphanumeric characters, and checks against a set of unsafe words. Discuss trade-offs between regex and manual tokenization, and mention edge cases like case sensitivity and Unicode.
Pro tip: Mention that you would preprocess the unsafe words into a set for O(1) lookups and use a regex with word boundaries (\b) to handle punctuation correctly, but be aware that \b may not work for all Unicode characters. Also, consider if the message can contain multiple words and if the unsafe words can be substrings of other words (e.g., 'damn' in 'damnation') — whole word matching avoids false positives.
Ask about input format, case sensitivity, definition of 'whole word', handling of punctuation, and whether unsafe words can contain punctuation. Confirm expected output format.
Decide between regex with word boundaries or tokenizing the message into words by splitting on non-alphanumeric characters. Consider performance and Unicode support.
For each message, extract words, convert to lowercase if case-insensitive, and check if any word is in the unsafe set. If none, include the message.
Validate with examples like 'damn,' 'damn!' 'damnation', and messages with multiple unsafe words. Ensure punctuation is handled and no false positives/negatives.
Talk about time/space complexity, precompiling regex, using sets for O(1) lookups, and potential issues with Unicode word boundaries.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.