Exam DP-203 topic 4 question 43 discussion

Seems correct: https://learn.microsoft.com/en-us/azure/databricks/optimizations/dynamic-file-pruning https://learn.microsoft.com/en-us/azure/databricks/delta/data-skipping

Dynamic file pruning, can significantly improve the performance of many queries on Delta Lake tables. Dynamic file pruning is especially efficient for non-partitioned tables, or for joins on non-partitioned columns. The performance impact of dynamic file pruning is often correlated to the clustering of data so consider using Z-Ordering to maximize the benefit.

By sorting the data files based on one or more columns, Z-Ordering can significantly improve the performance of queries that filter on those columns. Caching in Apache Spark allows frequently accessed data to be stored in memory, reducing the time it takes to read the data for subsequent operations. This can be particularly useful for speeding up joins and repeated queries against non-partitioned tables, as the data is readily available without having to be read from disk repeatedly.

https://www.databricks.com/blog/2020/04/30/faster-sql-queries-on-delta-lake-with-dynamic-file-pruning.html

correct