Snowflake SPS-C01 Exam : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark Python code snippet:

A)

B) This code will ovemrite the table if it already exists.
C) The code will fail because there is no call to or on the 'result_df dataframe and Snowflake performs lazy evaluation.
D) The 'result_df DataFrame will be persisted to the 'AGGREGATED SALES table in the default schema of the user running the code.
E) This code will fail because is not a valid method for Snowpark DataFrames.

2. You are profiling a Snowpark application that uses a combination of SQL queries and Python UDFs. You observe that a particular stage involving a UDF is taking significantly longer than expected. You suspect that the UDF's performance is the bottleneck. Which of the following steps would be the MOST comprehensive approach to diagnose and address the performance issue?

A) Increase the warehouse size and re-run the application. If the execution time improves significantly, the issue was resource contention.
B) Replace the Python UDF with an equivalent SQL query using Snowflake's built-in functions. If the SQL query performs better, the Python UDF was the bottleneck.
C) Convert the scalar UDF to a vectorized UDF, even without fully understanding the source of the performance bottleneck.
D) Implement caching for the UDF's results to avoid recomputing the same values multiple times.
E) Use Snowflake's query profile to examine the execution plan and identify the UDF-related stages with the highest execution time. Then, analyze the UDF's code for inefficiencies, such as unnecessary loops or complex calculations.

3. You are tasked with creating a series of Snowpark DataFrames for a data transformation pipeline. For debugging purposes, you want to materialize these DataFrames as tables within Snowflake, but only for the duration of your session. You also need to make sure that these tables are automatically cleaned up when your session ends. Which of the following approaches offer(s) the MOST efficient and appropriate way to achieve this?

A) Persist each DataFrame as a temporary table using using CTEs to perform the operations.
B) Persist each DataFrame using 'df.write.mode('overwrite').option('temporary', 'true').save_as_table(table_namey.
C) Persist each DataFrame using , and manually drop each table at the end of the session using 'session.sql(f'DROP TABLE {table_name}').collect()'.
D) Create each DataFrame as a local temporary view using and access these views via SQL within the same session.
E) Persist each DataFrame as a temporary table using , prepending a unique identifier to the table name to avoid naming conflicts.

4. You are developing a Snowpark application that requires calling a stored procedure. Which of the following approaches is the MOST secure and efficient way to call a stored procedure from your Snowpark Python code, assuming the stored procedure returns a single value?

A)

B)

C)

D)

E)

5. 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) You must use on the source DataFrame ('customers_df) before using 'merge' to ensure only one matching row exists per customer.
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) The merge operation cannot handle duplicate keys in the source DataFrame without pre-aggregation.
D) 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
E) 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.

Solutions: