When conducting data validation to ensure data accuracy and completeness, which of the following methods would best verify that all entries in a dataset are unique and non-duplicated?

Primary key constraints enforce uniqueness for each entry in a dataset by designating one or more columns as unique identifiers, ensuring that each row is distinct and non-duplicated. This method is effective for data validation, as it automatically flags duplicate entries upon insertion, thus preventing errors due to duplication. By establishing a primary key, the integrity and accuracy of the dataset are maintained, which is especially critical in relational databases where unique records are foundational for reliable data analysis. The other options are incorrect because: • Option 1 (Implementing cross-validation) is a method for model validation, not data validation. • Option 2 (Performing data imputation) addresses missing data, not duplicates. • Option 4 (Applying statistical sampling) helps estimate dataset properties but doesn’t ensure uniqueness. • Option 5 (Executing correlation analysis) evaluates relationships between variables, not entry uniqueness.