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Ryan, a Data Engineer, wants to improve the performance of large, complex queries against large data sets. He decided to Scale up underlying warehouse/cluster. What is correct Snowflake consid-eration while scaling up so that he can achieve better performance results? [Select all that apply]
Correct Answer: A,B,C,D,E
Explanation Resizing a warehouse generally improves query performance, particularly for larger, more complex queries. It can also help reduce the queuing that occurs if a warehouse does not have enough com-pute resources to process all the queries that are submitted concurrently. Note that warehouse resiz-ing is not intended for handling concurrency issues; instead, use additional warehouses to handle the workload or use a multi-cluster warehouse (if this feature is available for your account). Snowflake supports resizing a warehouse at any time, even while running. If a query is running slowly and you have additional queries of similar size and complexity that you want to run on the same warehouse, you might choose to resize the warehouse while it is running; however, note the following: Larger warehouse size is not necessarily faster; for smaller, basic queries that are already executing quickly, you may not see any significant improvement after resizing. Resizing a running warehouse does not impact queries that are already being processed by the warehouse; the additional compute resources, once fully provisioned, are only used for queued and new queries. Resizing between a 5XL or 6XL warehouse to a 4XL or smaller warehouse results in a brief period during which the customer is charged for both the new warehouse and the old warehouse while the old warehouse is quiesced.