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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. Consider the following Snowpark code snippet:

Which of the following statements are TRUE regarding the execution and performance of this code?

A) Removing 'cached_df = line would significantly improve the overall performance because caching always adds overhead.
B) The 'count? operation will use the cached results of and apply an additional filter on the cached data.
C) The 'filter' operation Ccol('column_a') > 100') will be executed twice.
D) The 'filter' operation Ccol('column_a') > 100') will be executed only once because 'cached_df stores the materialized result.
E) The 'countl' operation will trigger the materialization and caching of 'filtered_df.

2. You are developing a Snowpark application that needs to train a machine learning model on a large dataset stored in Snowflake. You want to optimize the performance of your model training process. Which of the following strategies would be MOST effective in leveraging Snowpark's architecture to achieve this, considering both client-side and server-side capabilities?

A) Create a UDF (User-Defined Function) that uses a pre-trained model stored locally on the client machine and applies it to the data within Snowflake.
B) Load the entire dataset into a Pandas DataFrame on the client machine and then use a local machine learning library (e.g., scikit-learn) to train the model.
C) Define a Snowpark DataFrame that transforms the data, then write that data back to Snowflake as a new table, then create a separate Python process running outside of Snowflake's environment to train the model.
D) Use Snowpark's 'sproc' feature to define a stored procedure that loads the data in chunks and trains the model directly within Snowflake's secure environment, leveraging its compute resources.
E) Use Snowpark to execute SQL queries to pre-aggregate the data on the server-side before transferring the smaller aggregated data to the client for model training.

3. A UDTF (User-Defined Table Function) named 'split_sentences" takes a text string as input and returns a table with each row containing a single sentence from the input. You need to grant SELECT privilege on this UDTF to a specific role, 'DATA ANALYST'. Which of the following SQL statements will achieve this?

A) GRANT USAGE ON FUNCTION TO ROLE DATA_ANALYST;
B) GRANT SELECT ON TABLE FUNCTION TO ROLE DATA_ANALYST;
C) GRANT ALL PRIVILEGES ON FUNCTION TO ROLE DATA_ANALYST;
D) GRANT EXECUTE ON FUNCTION TO ROLE DATA_ANALYST;
E) GRANT OWNERSHIP ON FUNCTION TO ROLE DATA ANALYST,

4. You have a Snowpark DataFrame named 'employee_df in VS Code. You want to define a user-defined function (UDF) in Python that calculates the bonus for each employee based on their salary and performance rating. The UDF should take the salary (SALARY) and performance rating (PERFORMANCE RATING) as inputs and return the bonus amount. Assume you have already established a Snowpark session. Which code snippet accurately defines and registers a UDF named 'calculate_bonus' that can be applied to the 'employee_df DataFrame?

A)

B)

C)

D)

E)

5. You are developing a Snowpark application that involves creating a set of stored procedures and UDFs to process data'. To ensure proper version control and dependency management, you decide to package your Python code into a single Python Wheel file and deploy it to Snowflake. Which of the following methods are valid for deploying and utilizing this Python Wheel file within Snowflake, considering best practices for maintainability and security? (Select TWO)

A) Use the 'snowflake-cli' to push the Python Wheel file as a package, then add the package name to the list of packages when creating the stored procedure or UDF.
B) Create a Conda environment file (environment.yml) that specifies the Python Wheel file as a dependency and use this file to create a Snowflake environment. Then, associate the stored procedures and UDFs with that environment.
C) Upload the Python Wheel file to an external stage (e.g., AWS S3) and configure Snowflake to access the external stage, then reference the wheel file path in the USING' clause of the UDF or stored procedure.
D) Use the Snowsight UI to upload the Python Wheel file as a dependency for the Snowflake environment, making it available for all stored procedures and UDFs within that environment.
E) Upload the Python Wheel file to an internal stage and directly reference it in the UDF or stored procedure definition using the 'USING' clause.

Solutions: