DEA-C02 Exam Dumps | You are using the Snowflake Spark connector to update records in a Snowflake table based on data from

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Question 105/149

You are using the Snowflake Spark connector to update records in a Snowflake table based on data from a Spark DataFrame. The Snowflake table 'CUSTOMER' has columns 'CUSTOMER ID' (primary key), 'NAME, and 'ADDRESS'. You have a Spark DataFrame with updated 'NAME and 'ADDRESS' values for some customers. To optimize performance and minimize data transfer, which of the following strategies can you combine with a temporary staging table to perform an efficient update?

A. Write the Spark DataFrame to a temporary table in Snowflake. Then, execute an 'UPDATE statement in Snowflake joining the temporary table with the 'CUSTOMER table using the 'CUSTOMER_ID to update the 'NAME and 'ADDRESS' columns. Finally, drop the temporary table.

B. Iterate through each row in the Spark DataFrame and execute an individual 'UPDATE statement against the 'CUSTOMER table in Snowflake. Use the 'CUSTOMER_ID in the 'WHERE clause.

C. Broadcast the Spark DataFrame to all executor nodes, then use a UDF to execute the 'UPDATE' statement for each row directly from Spark.

D. Write the Spark DataFrame to a temporary table in Snowflake using MERGE. Use the WHEN MATCHED clause for Update the target table based on updates from staging table and finally drop the staging table

E. Use Spark's foreachPartition to batch update statements and execute on each partition. This will help with efficient data transfer and avoid single row based updates.

Correct Answer: A,D

Options A and D are the most efficient. Writing to a temporary table allows Snowflake to perform the update operation using its optimized internal processes. The MERGE command is designed for efficient upserts and is preferred over individual UPDATE statements. Option B is highly inefficient due to the overhead of multiple individual queries. Option C is also not optimal, as UDFs don't necessarily improve performance for simple UPDATE operations and broadcasting data is not needed. Option E can also be an approach for batch update, its effective way for the performance but its less compared with option A and D.

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