A data science team is developing an automated ingestion pipeline for customer feedback data provided as CSV files. The pipeline frequently fails due to parsing errors, specifically when feedback text contains commas or line breaks. Although the text fields are enclosed in double quotes as per convention, the parser still misinterprets the data structure. Which of the following is the most likely underlying cause of this data ingestion problem?
The data provider is using a regional-specific delimiter, such as a semicolon, instead of a comma.
The CSV files contain unescaped double quotes within data fields that are also enclosed in double quotes.
The CSV files are being saved with a UTF-8 byte-order mark (BOM) that the ingestion script cannot interpret.
The ingestion pipeline is attempting to infer a data schema, and the presence of mixed data types is causing type-casting failures.
The correct answer identifies that the most probable cause is the presence of unescaped double quotes within fields that are already quoted. According to RFC 4180, a common convention for CSV files, if a field is enclosed in double quotes to handle special characters like commas or line breaks, any double quote character within the field's content must be escaped by preceding it with another double quote. Failure to do so confuses the parser, which interprets the unescaped quote as the end of the field, leading to structural errors.
The use of a UTF-8 BOM is a common issue but typically causes the entire file to be misread from the start or results in garbled characters, not intermittent parsing failures based on specific field content.
An incorrect delimiter, like a semicolon, would cause every line to be parsed incorrectly, not just the lines where specific characters appear within the text fields.
Type-casting failures occur after the file has been successfully parsed into a tabular structure and the system attempts to assign data types. The problem described is a parsing failure, which happens before type inference.
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