Free Access Snowflake.SPS-C01.v2026-07-23.q130 Practice Test (Page 6)

Question 21

You are working with a Snowpark DataFrame named 'employees_df' that contains employee data, including a column named 'hire_date' of type String. The date format in the 'hire_date' column is 'MM/DD/YYYY'. You need to convert this column to a DateType so you can perform date-based calculations. Which of the following Snowpark code snippets correctly converts the column to a DateType?

Question 22

Consider the following Snowpark Python stored procedure:

What steps are necessary to register this Python code as a stored procedure named 'GET ROW COUNT in Snowflake and allow users with the 'ANALYST' role to execute it, assuming the stored procedure will be created with the 'EXECUTE AS OWNER clause, and the table name parameter will be passed dynamically during invocation?

A.1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON IMPORTS-('/path/to/dependencies.zip') EXECUTE AS OWNER;' 2. Grant 'USAGE' privilege on the database and schema containing the stored procedure to the 'ANALYST' role. 3. Grant 'EXECUTE PROCEDURE privilege on the 'GET ROW COUNT' stored procedure to the 'ANALYST role.

B.1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON EXECUTE AS OWNER AS $$ import snowflake.snowpark as snowpark def snowpark.Session, table_name: str) str: df = session.table(table_name) count = df.count() return f" Table {table_name} has {count} rows." $$; 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant 'EXECUTE PROCEDURE privilege on the 'GET ROW COUNT stored procedure to the 'ANALYST role.

C.1. create the stored procedure using 'CREATE OR REPLACE PROCEDURE RETURNS VARCHAR LANGUAGE PYTHON RUNTIME_VERSlON=3.8 HANDLER='my_sproc' EXECUTE AS OWNER$ 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. The 'ANALYST' role does not need any additional privilege if EXECUTE AS OWNER is set, as it's using the owner's access rights.

D.1. Create the stored procedure using 'CREATE OR REPLACE PROCEDURE GET ROW COUNT(VARCHAR) RETURNS VARCHAR LANGUAGE PYTHON EXECUTE AS CALLER;' 2. Grant 'USAGE privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant 'SELECT' privilege on all possible tables referenced by 'table_name' to the 'ANALYST' role.

E.1. create the stored procedure using 'CREATE OR REPLACE PROCEDURE RETURNS VARCHAR LANGUAGE PYTHON IMPORTS-('/path/to/dependencies.zip') EXECUTE AS OWNER;' 2. Grant 'USAGE-' privilege on the database and schema containing the stored procedure to the 'ANALYST role. 3. Grant ' EXECUTE privilege on the stored procedure to the 'ANALYST role.

Question 23

You are developing a Snowpark application to process customer reviews. You need to use a third-party sentiment analysis library, 'SentimentAnalyzer', which is NOT available in the Anaconda repository. You have the library JAR file stored in an internal artifact repository accessible via HTTP. Which of the following steps are necessary to make this library available to your Snowpark session?

A.Upload the JAR file toa Snowflake stage and register it as a Java UDF using CREATE FUNCTION.

B.Use 'session.add_dependency('/path/to/SentimentAnalyzer.jar')' in your Snowpark Python code after uploading the JAR to an internal stage.

C.Configure the Snowflake account-level parameter to point to the HTTP location of the JAR file. Then use session.add_import' to use it.

D.Upload the JAR file to a Snowflake stage. Then use 'session.add_import' to make the file available in your Snowpark session.

E.Create a conda environment that includes the JAR, upload it to a stage, and use the environment in Snowpark.

Question 24

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 using , and manually drop each table at the end of the session using 'session.sql(f'DROP TABLE {table_name}').collect()'.

B.Persist each DataFrame as a temporary table using , prepending a unique identifier to the table name to avoid naming conflicts.

C.Persist each DataFrame as a temporary table using using CTEs to perform the operations.

D.Persist each DataFrame using 'df.write.mode('overwrite').option('temporary', 'true').save_as_table(table_namey.

E.Create each DataFrame as a local temporary view using and access these views via SQL within the same session.

Question 25

You have a Snowpark DataFrame named 'products' with columns 'product_id' (INT), 'product_name' (STRING), and 'price' (DOUBLE). You want to apply a transformation to calculate a 'discounted_price' column, which is the 'price' reduced by 10% if the price is greater than $100.00. Which of the following code snippets is the most efficient way to achieve this using Snowpark Python?