A table, TABLE SALES, is reloaded daily, and is used by many users to support multiple data reporting tools. Users report poor performance when querying the table. This is the clustering information for TABLE SALES: { "cluster_by_keys" : "LINEAR(SALES_DT)", "total_partition_count" : 10798, "total_constant_partition_count" : 0, "average_overlaps" : 10797.0, "average_depth" : 10798.0, "partition_depth__histogram" : { "00000" : 0, "00001" : 0, "00002" : 0, "00003" : 0, "00004" : 0, "00005" : 0, "00006" : 0, "00007" : 0, "00008" : 0, "00009" : 0, "00010" : 0, "00011" : 0, "00012" : 0, "00013" : 0, "00014" : 0, "00015" : 0, "00016" : 0, "16384" : 10798 }, "clustering_errors" : [ ] } What step will improve the performance of the micro-partitions and OPTIMIZE query performance?
Select an option, then click Submit answer.
- ○ Incorporate additional pruning options when the table is being accessed by reporting queries.
- ○ Identify and purge duplicate data in the table.
- ○ Use the ORDER BY SALES_DT parameter when inserting the data into the table.
- ○ Perform a one-time insert overwrite with the ORDER BY SALES_DT parameter.
