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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark code snippet designed to process data from an event table and calculate a rolling average:

After deploying this code, you observe that the rolling average calculation is significantly slower than expected, even though the virtual warehouse is adequately sized. What is the MOST effective way to optimize the performance of this rolling average calculation in Snowpark, considering the size of window_size ?

A) Reduce the 'window_size' parameter. Smaller windows inherently require less computation.
B) Materialize the intermediate 'event_df DataFrame into a temporary table using before applying the window function.
C) Use the SNOWFLAKML.FORECAST package in the Snowpark DataFrame to forecast the rolling average of events in each of the user session.
D) Utilize Snowflake's APPROX AVG aggregate function in conjunction with a custom UDF to estimate the rolling average, trading off accuracy for performance.
E) Use the 'rangeBetween' method instead of 'rowsBetween' in the 'Windows specification, as it is generally more efficient for numerical data.

2. You have a SQL query stored in a file named 'query.sqr which contains several complex analytical calculations. The query depends on a Snowpark 'session' object already established. You want to create a Snowpark DataFrame from the result of this query. Which of the following code snippets achieves this with optimal performance and readability, assuming correct file access permissions?

A)

B)

C)

D)

E)

3. You have a Snowpark Python stored procedure named 'calculate_stats' that takes a table name as input and returns summary statistics. You need to modify the stored procedure to add a new optional parameter for specifying a filter condition. Which of the following SQL commands, used in conjunction with the Snowpark API for Python, is the MOST efficient way to alter the existing stored procedure without dropping and recreating it?

A)

B)

C)

D)

E)

4. 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) 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
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) You must use on the source DataFrame ('customers_df) before using 'merge' to ensure only one matching row exists per customer.

5. You are working with two large Snowpark DataFrames: 'transaction_df and 'product df. 'transaction_df contains transaction data including 'transaction id', 'product id', and 'transaction_date'. 'product df contains product details including 'product id', product_name', and 'product category'. You need to join these DataFrames to analyze transaction data by product category. The 'transaction_df is significantly larger than 'product_df. Which of the following strategies can significantly improve the performance of the join operation in Snowpark? (Select all that apply)

A) Use a broadcast join by explicitly specifying 'broadcast-True in the 'join' function when joining 'product_df to 'transaction_df.
B) Cache the 'transaction_df DataFrame before the join operation using
C) Filter the 'transaction_df to a smaller subset based on 'transaction_date' before performing the join, if only recent transactions are needed.
D) Use a 'hint' to force Snowflake to use a specific join algorithm like 'MERGE JOINS.
E) Ensure that the 'product_id' column in both DataFrames is of the same data type and has statistics collected on it.

Solutions: