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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A Snowpark developer is using to create a Snowpark session. They want to ensure that the session uses a specific role and warehouse, but only if those parameters are not already defined in the Snowflake CLI configuration. Which of the following code snippets correctly implements this behavior?

A)

B)

C)

D)

E)

2. You are developing a Snowpark Python application that performs advanced machine learning model training on a large dataset stored in Snowflake. You observe that the application is memory-intensive, causing frequent spilling to disk and slowing down the training process.
Which of the following strategies, when implemented in conjunction, is MOST likely to improve the performance of your Snowpark application in this memory-constrained scenario?

A) Increase the 'MAX CONCURRENCY LEVEL' parameter at the session level and rewrite the model training code in SQL using stored procedures.
B) Switch to a larger Snowflake warehouse size and increase the parameter to ' True'.
C) Enable caching for intermediate results and switch to a Snowpark-optimized warehouse with a smaller size to force early spilling to disk.
D) Reduce the batch size for data loading, utilize Snowpark's optimized data types for columns with small value ranges, and use memory profiling tools to identify memory leaks in your Python code and optimize the application.
E) Increase the size of the Snowpark-optimized warehouse with 'MEMORY OPTIMIZED server type, use appropriate data types to minimize memory footprint, and optimize UDFs to minimize intermediate data creation.

3. You are tasked with optimizing a Snowpark Python stored procedure that performs complex data transformations on a DataFrame. The procedure frequently encounters out-of-memory errors when processing large datasets. Which of the following strategies could you implement to mitigate these memory issues within the stored procedure's code ? Choose all that apply.

A) Implement data filtering and aggregation as early as possible in the transformation pipeline to reduce the size of the DataFrame.
B) Leverage the 'sample()' function to work with a smaller subset of the data for testing and debugging.
C) Use smaller data types (e.g., ' Int16' instead of ' Int64') where appropriate to minimize memory footprint.
D) Utilize the 'repartition()' or functions to control the number of partitions in the DataFrame and potentially reduce memory consumption per partition.
E) Increase the warehouse size to provide more memory resources.

4. 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 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.
B) 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.
C) 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.
D) 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.
E) 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.

5. A data engineering team wants to create a Snowpark stored procedure that takes a VARIANT column from a Snowflake table, parses a specific JSON element within each row, and returns a new DataFrame with the extracted data as a STRING column. The JSON structure is consistent across all rows. What is the MOST efficient and type-safe way to implement this, considering the need for performance and maintainability?

A) Use Snowpark's 'get' function within the stored procedure to extract the JSON element, explicitly cast the extracted value to STRING using 'cast('string')' , and register the stored procedure with defining the output schema.
B) Use Python type hints for the input VARIANT column, extract the JSON element using string manipulation within the stored procedure, and return a DataFrame with the extracted data as a string.
C) Use the 'get' function on the VARIANT column to extract the JSON element, use the 'as_varchar' function to cast the VARIANT value to a String value, and register the stored procedure with explicit 'return_type' and schema definition for enhanced type safety
D) Define the input column using and use the operator to implicitly convert the extracted JSON element to a string, relying on Snowpark's type inference for the return type.
E) Define the input column as a generic 'object' type in Python, use Snowpark's 'get function with path navigation to extract the JSON element, and return the extracted data as a string using 'as_varchar'.

Solutions: