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    Question

    A data analyst is assessing a dataset with inconsistent

    categorical entries, such as "USA," "U.S.A," "United States," and "US" for the country field. Which of the following is the best approach for handling this inconsistency?
    A Filtering duplicates based on the entire row Correct Answer Incorrect Answer
    B Using normalization to scale data entries Correct Answer Incorrect Answer
    C Applying data transformation to encode all entries numerically Correct Answer Incorrect Answer
    D Standardizing categorical entries to a single representation Correct Answer Incorrect Answer
    E Converting all entries to uppercase to eliminate case sensitivity Correct Answer Incorrect Answer

    Solution

    Standardizing categorical entries to a single representation ensures consistency by consolidating multiple formats of the same entity into one standardized label. For example, consolidating "USA," "U.S.A," "United States," and "US" into one uniform label, like "United States," ensures that all data entries are interpreted consistently. This process is essential in data cleaning, as inconsistencies in categorical data can lead to inaccurate analysis, skewed results, and duplications in reporting. A uniform categorical format enables reliable grouping, sorting, and filtering for analysis. The other options are incorrect because: • Option 1 (Filtering duplicates) removes identical rows but doesn’t address inconsistency in a single field. • Option 2 (Using normalization) only applies to numeric scaling, not categorical consistency. • Option 3 (Applying data transformation) would encode inconsistencies rather than correct them. • Option 5 (Converting to uppercase) helps with case sensitivity but does not fully standardize variations.

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