Which statement about Null usage is true?

Which statement about Null usage is true?

• A. Null values can contain empty strings for numeric columns
• B. Null values are used to avoid confusion about missing data
• C. Null can have empty strings or placeholders for numeric columns
• D. Null values indicate an error in data entry

Answer: (C)

Null marks a value as missing or unknown. It’s a distinct marker that signals “no data here” rather than any actual value like zero or an empty string. In real-world data handling, you might encounter scenarios where missing numeric data is represented by placeholders (such as an empty string) during data entry or during import/export workflows. The idea is that these placeholders stand in for the absence of a value, and later processes should convert them to Null to reflect the missing data properly. In a properly designed database, you would store Null in a numeric column to indicate missing data, since an empty string is not a valid numeric value. So the statement aligns with the practical use of Null as an indicator of missing information and recognizes that placeholders can appear in some data pipelines. Null does not imply an data-entry error by itself, and the notion that Null can be an actual value like an empty string is not correct in typical relational databases.

Null marks a value as missing or unknown. It’s a distinct marker that signals “no data here” rather than any actual value like zero or an empty string. In real-world data handling, you might encounter scenarios where missing numeric data is represented by placeholders (such as an empty string) during data entry or during import/export workflows. The idea is that these placeholders stand in for the absence of a value, and later processes should convert them to Null to reflect the missing data properly. In a properly designed database, you would store Null in a numeric column to indicate missing data, since an empty string is not a valid numeric value. So the statement aligns with the practical use of Null as an indicator of missing information and recognizes that placeholders can appear in some data pipelines. Null does not imply an data-entry error by itself, and the notion that Null can be an actual value like an empty string is not correct in typical relational databases.