DEA-C02 Exam Dumps | A Snowflake data engineer is troubleshooting a slow-running query that joins two large tables, 'ORDERS'

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Question 53/149

A Snowflake data engineer is troubleshooting a slow-running query that joins two large tables, 'ORDERS' (1 billion rows) and 'CUSTOMER' (10 million rows), using the 'CUSTOMER ID' column. The query execution plan shows a significant amount of data spilling to local disk. The query is as follows:

Which of the following are the MOST likely root causes of the disk spilling and the best corresponding solutions? Select two options that directly address the disk spilling issue.

A. The virtual warehouse is undersized for the amount of data being processed. Increase the virtual warehouse size to provide more memory.

B. The 'CUSTOMER_ID column is not properly clustered in either the 'ORDERS' or 'CUSTOMER table. Define a clustering key on 'CUSTOMER_ID for both tables.

C. The query is performing a full table scan on the 'ORDERS' table. Add an index on the 'CUSTOMER ID column in the 'ORDERS table.

D. The join operation is resulting in a large intermediate result set that exceeds the available memory. Apply a filter on the 'ORDERS' table to reduce the data volume before the join.

E. The statistics on the tables are outdated. Run 'ANALYZE TABLE ORDERS' and 'ANALYZE TABLE CUSTOMER to update the statistics.

Correct Answer: A,D

Options A and D are the most direct solutions for disk spilling. A undersized warehouse directly impacts available memory, leading to disk spilling. Increasing the warehouse size (option A) provides more memory for the operation. When data spill happens increasing the warehouse size is the primary action to take. Option D correctly addresses the root cause of the spill an overly large intermediate result set. Reducing the data volume before the join minimizes the memory required. Option B could improve query performance overall, but doesn't directly address disk spilling. Option C is incorrect, as Snowflake does not support manual indexes. Option E would improve the accuracy of the query optimizer's decisions, which could indirectly improve performance, but is less direct than options A and D.

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