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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark Python code snippet designed to create a DataFrame and then register a custom function (UDF):

This code runs successfully. However, you need to deploy this as a stored procedure. What minimal changes are required to make this code runnable as a Snowpark Python stored procedure and callable from SQL?

A) No changes are required; the code will function as a stored procedure as is.
B) The 'return df.collect()' line must be replaced with 'return and the 'return_type' and 'input_typeS arguments of udf must be removed to allow inference. The rest of the code remains unchanged.
C) The 'return df.collect()' line must be replaced with 'return and the Snowflake session object must be explicitly passed to the UDF when it is called.
D) The 'return df.collect()' line must be replaced with 'return df and create a DataFrame. The rest of the code remains unchanged.
E) The 'return df.collect()' line must be replaced with 'return and 'return_type' and 'input_typeS arguments of udf must be removed. The rest of the code remains unchanged.

2. You are tasked with creating a secure UDF in Snowflake using Snowpark Python API that encrypts sensitive customer data before storing it. The UDF should be accessible only to users with specific roles. You have the following code snippet. What needs to be done to make it secure and operationalize for multiple users?

Considering Security best practices, what steps are critical for securing the UDF and granting appropriate permissions to other users?

A) Bypass role-based access control and use shared credentials for UDF execution.
B) Use Snowflake's Secret object to securely store the encryption key and retrieve it within the UDF. Grant USAGE privilege on the secret to specific roles.
C) Store the encryption key directly in the UDF code for simplicity.
D) Grant global USAGE privilege on the function's stage location to all users.
E) Grant EXECUTE privilege on the UDF and USAGE privilege on the database and schema containing the UDF to the roles needing access.

3. You are building a Snowpark application using the 'snowflake-cli' to manage Snowflake connections. You have configured multiple connection profiles using 'snowflake connection add'. Which of the following Python code snippets demonstrates the most efficient and idiomatic way to create a Snowpark session using a specific connection profile named 'my_profile' defined in your 'snowflake-cli' configuration? Assume snowflake-cli is correctly configured and authenticated.

A)

B)

C)

D)

E)

4. You have two Snowpark DataFrames: 'employees_df with columns 'employee_id' (INTEGER), 'employee_name' (STRING), 'department_id' (INTEGER), and 'salaries_df' with columns 'employee_id' (INTEGER), "salary' (FLOAT), 'effective_date' (DATE). You need to create a new DataFrame that contains the employee's name, department, and the highest salary they have ever received. Assuming there can be multiple salary entries for the same employee with different 'effective date' values, which of the following Snowpark code snippets would correctly and efficiently solve this problem?

A)

B)

C)

D)

E)

5. You have a Snowpark DataFrame 'df_orders' containing order data'. You want to delete all records from the underlying Snowflake table 'ORDERS TABLE' where the 'order_date' is older than '2023-01-01' using a Snowpark DataFrame operation. Which of the following code snippets is the MOST efficient and recommended way to achieve this, assuming 'spark' is your Snowpark Session object?

A) Option B
B) Option A
C) Option C
D) Option D
E) Option E

Solutions: