Which method is used to reduce the number of records in a dataset by removing specific rows?

The method used to reduce the number of records in a dataset by removing specific rows is filtering. Filtering allows users to specify certain criteria or conditions that rows must meet to be included in the resulting dataset. Consequently, any rows that do not satisfy those conditions are excluded from the final output. This is particularly valuable when working with large datasets, as it enables focused analysis on only the relevant records based on specific attributes or values.

In contrast, other methods such as grouping, merging, or summarizing serve different purposes. Grouping is used to combine rows based on common attributes, creating a summary view but not necessarily reducing the dataset by removing rows. Merging involves combining datasets based on common fields and does not filter out rows but instead combines them based on specified criteria. Summarizing aggregates data to provide insights such as totals or averages but does not inherently focus on removing rows from the dataset. Thus, filtering stands out as the correct approach for the specific task of reducing records by removing unnecessary rows.