The effects of query pruning can be observed by evaluating which statistics? (Choose two.)
The effects of query pruning can be observed by evaluating which statistics? (Choose two.)
The effects of query pruning can be observed by evaluating 'Partitions scanned' and 'Bytes scanned' statistics. 'Partitions scanned' indicates the number of data partitions that were actually accessed during the execution of a query, with efficient pruning reducing the number of partitions scanned. 'Bytes scanned' shows the volume of data scanned to fulfill the query, and effective pruning reduces the amount of data scanned by skipping over irrelevant data, thus enhancing query performance.
AB - correct https://docs.snowflake.com/en/user-guide/ui-query-profile
AC is correct A. Partitions scanned: This statistic indicates the number of data partitions (or micro-partitions in Snowflake's terms) that were actually accessed during the execution of a query. Efficient query pruning will reduce the number of partitions scanned because it avoids reading data that does not match the query's filters. A lower number compared to the total available partitions can show that query pruning has effectively reduced the dataset that needed to be scanned. C. Bytes scanned: This statistic shows the volume of data scanned to fulfill the query. Just like with partitions scanned, effective query pruning should reduce the amount of data that needs to be scanned, as it skips over blocks of data that are determined (via metadata inspection) not to contain relevant data. This can significantly enhance query performance, especially in large datasets.