DEA-C02 Exam Dumps | You are using Snowpark to perform a complex join operation between two large tables: 'ORDERS (1 OOGB)

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Question 5/149

You are using Snowpark to perform a complex join operation between two large tables: 'ORDERS (1 OOGB) and 'CUSTOMER (50GB). The join is performed on 'ORDERS.CUSTOMER ID = CUSTOMER.ID. The query is running slower than expected. You have already confirmed that the warehouse size is adequate. Which of the following strategies, applied in combination , would most likely improve the join performance within a Snowpark context?

A. Ensure both tables are clustered on the join keys CORDERS.CUSTOMER_ID' and 'CUSTOMER.ID').

B. Use Snowpark's 'hint function to force a broadcast join, assuming the 'CUSTOMER table can fit into memory on the worker nodes.

C. Increase the 'AUTO RESIZE' setting on the warehouse to automatically scale up the warehouse size when the load increases.

D. Analyze the query profile in Snowflake's web UI to identify the specific bottleneck (e.g., excessive data spilling, high CPU utilization) and address it directly.

E. Use 'session.add_import to add external JAR dependencies. This would enable use of external libraries and improve performance.

Correct Answer: A,D

Options A and D are the most effective in combination. Clustering both tables on the join keys allows Snowflake to efficiently locate and join related rows, drastically reducing the amount of data that needs to be scanned. Analyzing the query profile (D) allows you to pinpoint the exact bottleneck. Broadcasting the customer table might help if it's small enough (B), but it's risky without proper analysis. Auto-resizing (C) might help but is not a targeted solution and can lead to higher costs. Importing JARs (E) is unrelated to core join performance optimization unless custom join logic is needed which it isn't in this case.

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