How does a Partition Primary Index (PPI) influence data organization?

The choice that states a Partition Primary Index (PPI) sorts rows by partition number accurately captures the role of PPI in data organization within a Teradata system. A PPI is designed to organize data into distinct partitions based on the specified index columns. This segmentation allows for more efficient data management and retrieval because each partition can be processed independently. As a result, when data is inserted or queried, Teradata will sort the rows according to the specified partitioning criteria, effectively grouping similar data together within each defined partition.

This organization aids query performance, particularly in scenarios where only specific segments of data need to be accessed. By sorting rows in this manner, the database system can quickly locate and retrieve the relevant data without scanning entire tables, leading to improved efficiency in data operations.

While the other statements touch upon concepts related to data organization or indexing, they do not accurately reflect the primary purpose of a Partition Primary Index in organizing the dataset based on partitioning. For example, duplicating rows or ensuring uniqueness is not a fundamental characteristic of PPI, and the restriction of columns is more related to index design rather than the sorting mechanism inherent to PPI.