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Nominal level measurement provides exclusive and exhaustive categories without rank, making it the correct response. This type of data classification groups variables into distinct categories that are mutually exclusive, meaning that each observation belongs to one and only one category. Additionally, these categories cover all possible responses within the context of the data collection, hence being exhaustive.

Nominal data does not imply any order among the categories; for instance, gender, race, or types of fruit are examples where the categories can be named but are not ranked in any way. Each category is considered equal with no quantitative value assigned.

In contrast, the other classifications described involve various forms of ranking or numerical representation that are not applicable to nominal data. For example, ranked categories with measurable distances imply an ordinal or interval level of measurement where order and measurable differences exist, which is not the case with nominal data. This distinction highlights why nominal classification focuses solely on categorization without the nuances of rank or measurement that other types involve.