SPS-C01 Exam Dumps | You have two Snowpark DataFrames: containing customer information and 'orders

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Snowflake.SPS-C01.v2026-08-07.q151
Question 36

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Question 36/151

You have two Snowpark DataFrames: containing customer information and 'orders_df containing order details. You need to merge these DataFrames based on the column to create a unified view. The 'customers_df may contain duplicate values. The contains recent orders. You want to use 'merge' with an 'UPDATE action based on 'customer id' and a 'WHEN NOT MATCHED BY TARGET action to insert new customer records from 'customers df into the 'orders df table that do not exist.

A. The merge operation cannot handle duplicate keys in the source DataFrame without pre-aggregation.

B. The 'UPDATE action in 'merge' will update all matching rows in the target table based on the join condition, regardless of duplicates in the source.

C. You must use on the source DataFrame ('customers_df) before using 'merge' to ensure only one matching row exists per customer.

D. The 'merge' operation will fail immediately upon encountering the first duplicate key during the update operation. Consider stage the source dataframe and use an appropriate dedup option with merge.

E. The 'merge' operation will succeed, processing each matching record sequentially. If more than one row matches it will process based on order in the dataframe

Correct Answer: A

Snowflake's 'MERGE statement, including the Snowpark DataFrame 'merge' functionality, requires unique matching keys on the source side when performing updates. Duplicates in the source can lead to unpredictable behavior and incorrect updates. While the operation might execute without an explicit error, the results will be unreliable. Pre-aggregation or deduplication of the source DataFrame is necessary before using 'merge' in this scenario to ensure each target row is updated correctly. This is crucial for data integrity and consistent results when performing DML operations with Snowpark. Although it may process without explicit error, it will process each matching record based on order in the source which can lead to unexpected results. Therefore, it's best practice to dedup or pre-aggregate.

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