This sounds like a warmup but quoted fields with commas inside them will wreck you if you just split on commas naively.
Start by clarifying the requirements: what defines an empty field (e.g., whitespace-only?), how to handle quoted fields with embedded commas or newlines, and whether to report or filter. Then outline a line-by-line parsing strategy that correctly handles CSV quoting, validates each field, and collects or skips invalid rows. Finally, discuss trade-offs between reporting and filtering, and how to handle edge cases like headers or trailing newlines.
Pro tip: Demonstrate awareness of real-world CSV complexities: mention that naive splitting on commas fails with quoted fields containing commas or newlines, and that using a proper CSV parser (or implementing a state machine) is essential. Also, consider performance implications for large inputs and suggest streaming to avoid loading everything into memory.
Ask about the definition of 'empty' (e.g., empty string vs. whitespace-only), whether the first row is a header, and how to handle quoted fields with embedded delimiters or newlines. Confirm whether the output should be a list of invalid rows or just a filtered valid set.
Decide between using a built-in CSV library or implementing a simple state machine to correctly parse quoted fields. For interviews, a state machine shows deeper understanding, but mention library trade-offs (e.g., speed, correctness).
For each parsed row, check every field against the emptiness condition. If any field is empty, mark the row as invalid. Consider whether to trim whitespace before checking.
If reporting, collect invalid rows with line numbers and reasons. If filtering, skip invalid rows and return only valid ones. Discuss whether to fail fast or process all rows.
Address trailing newlines, empty lines, and large inputs. Suggest streaming line-by-line to avoid memory issues, and mention how to handle malformed CSV (e.g., unclosed quotes).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Felt straightforward until I started second-guessing whether the match should be case-insensitive, substring vs whole-word, etc.
Start by clarifying the problem: which fields are relevant, how to handle case sensitivity, and whether partial matches count. Then propose an efficient solution using a set for O(1) lookups and discuss trade-offs like memory vs. speed, and edge cases like quoted fields in CSV.
Pro tip: Mention that you'd preprocess the banned words into a set and consider using a trie or Aho-Corasick if partial matches are needed, showing awareness of algorithmic trade-offs. Also, emphasize the importance of handling CSV parsing correctly (e.g., using a library) to avoid subtle bugs with commas in quoted fields.
Ask about the definition of 'relevant field', case sensitivity, partial vs. exact matches, and performance constraints. Confirm the expected output format.
Use a set for banned words for O(1) exact-match lookups. If partial matches are needed, consider a trie or Aho-Corasick automaton for efficient multi-pattern matching.
Use a robust CSV parser to handle quoted fields, escaped characters, and delimiters. Avoid naive splitting by commas.
For each row, extract the relevant field(s), normalize case if needed, and check against the banned words set. If a match is found, skip the row.
Talk about time/space complexity, handling large files (streaming vs. loading all into memory), and potential false positives/negatives.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Tokenization rules are where it gets genuinely interesting.
Start by clarifying the tokenization requirements: what defines a token (e.g., whitespace, punctuation, case sensitivity) and how to handle edge cases like empty strings or non-string values. Then propose a tokenization strategy that balances simplicity and correctness, such as using a regex-based splitter or a library function, and explain how you would efficiently count stop-word matches across the filtered rows.
Pro tip: Mention that you would normalize tokens (e.g., lowercase, strip punctuation) and use a set for stop-words to achieve O(1) lookups, showing awareness of performance and data quality. Also, discuss how you would handle tokenization for different languages or special characters if the data is international, demonstrating foresight.
Ask about the definition of a token, expected data types, language/locale, and performance constraints. Confirm whether tokenization should be case-insensitive and how to treat punctuation and special characters.
Select a tokenization approach: simple whitespace split, regex-based splitting on word boundaries, or a library like NLTK for advanced cases. Justify your choice based on the clarified requirements.
Normalize tokens (e.g., lowercase, remove punctuation) and load stop-words into a set for fast membership testing. Consider if stop-words need similar normalization.
Iterate over the filtered rows, tokenize the two designated columns, and for each token check if it's in the stop-words set. Increment a counter for each match, ensuring no double-counting if the same token appears multiple times.
Address trade-offs: regex vs. simple split, memory vs. speed, handling large datasets via streaming or parallelization. Mention potential pitfalls like Unicode normalization or stemming/lemmatization if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.