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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark Python code snippet intended to operationalize a stored procedure:

This code results in an error during registration. Which of the following actions would be MOST appropriate to fix the registration issue and ensure the stored procedure can be successfully operationalized?

A) Define 'session' as a global variable before calling 'session.sproc.register' .
B) Remove the 'input_data' parameter from the 'main' function definition and access the input data directly using within the function.
C) Add in sproc.register.
D) Move the 'session.sproc.register' call outside of the 'main' function and execute it separately after the 'main' function definition.
E) Add 'session.close()' at the end of the main function.

2. You have a Snowpark DataFrame named containing daily sales transactions. The DataFrame includes columns like 'transaction_id' , 'product id', 'sale_date', and 'sale_amount'. You need to perform the following transformations and persist the results: (1) Calculate the total sales amount for each product on a daily basis. (2) Store the aggregated results into a new table named , partitioned by 'sale_date'. (3) Ensure that if the table already exists, the new data is appended to the existing table. Which of the following code blocks achieve these requirements in the most efficient and correct manner?

A)

B)

C)

D)

E)

3. Consider a Snowpark DataFrame with columns 'DEPARTMENT, 'SALARY , and 'YEAR. You want to find the average salary for each department over all years and then filter the departments to only include those where the average salary is greater than 100000. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark Python?

A)

B)

C)

D)

E)

4. You have a Snowpark DataFrame containing product information, and you want to persist it into a Snowflake table named PRODUCTS. You need to handle the following scenarios: 1. If the table 'PRODUCTS does not exist, create it. 2. If the table PRODUCTS' exists, append the data from 'df_products' to it. Which of the following methods can achieve this?

A)

B)

C)

D)

E)

5. You are working with Snowpark DataFrames representing sales transactions. The 'transactions df DataFrame contains recent transactions, while the 'sales_table' in Snowflake holds the historical sales data'. You need to merge the new transactions into the 'sales table', but you want to track which rows were inserted, updated, or left unchanged by the 'merge' operation. How can you capture this information using Snowpark and persist it to a separate table?

A) You can use the 'returning' clause within the merge statement to retrieve the impacted rows and their change status (INSERTED, UPDATED) and then write this data to a tracking table using 'write.saveAsTable' .
B) The merge operation automatically creates a system table or view that logs the details of each row that was inserted, updated, or left unchanged.
C) You can combine the 'returning' clause along with a stored procedure to retrieve the inserted/updated rows with their status and then use the stored procedure to write to a different tracking table using insert statement.
D) It's not possible to capture the merge operation details (inserted/updated/unchanged rows) directly using Snowpark alone. You would need to implement custom logic outside of Snowpark to compare the data before and after the merge.
E) After the merge, query the 'sales_table' and compare it to a copy of the 'sales_table' taken before the merge. Identify inserted rows as those present in the new version but not in the old, and updated rows as those with different values in specific columns between the two versions. This strategy should be implemented with the help of external functions and UDFs, as it is an expensive operation and should not be computed with normal SQL.

Solutions: