Free SPS-C01 sample questions

Correct answers: User-Defined Table Function (UDTF) for requirement 1; Snowpark Stored Procedure for requirement 2

A UDTF (User-Defined Table Function) is the correct object for requirement 1, as it takes zero, one, or multiple input rows and returns a tabular output (multiple rows/columns), which is necessary for parsing a single string into multiple rows. A Stored Procedure is the correct object for requirement 2, as it allows for control-flow logic, transaction management, and orchestrating multiple SQL operations natively within the Snowflake engine.

A UDTF (User-Defined Table Function) is the correct object for requirement 1, as it takes zero, one, or multiple input rows and returns a tabular output (multiple rows/columns), which is necessary for parsing a single string into multiple rows. A Stored Procedure is the correct object for requirement 2, as it allows for control-flow logic, transaction management, and orchestrating multiple SQL operations natively within the Snowflake engine.

Correct answer: The standard Python `math.sqrt()` executes entirely on the client side requiring data to be pulled into local memory, whereas `snowflake.snowpark.functions.sqrt()` pushes the computation down to the Snowflake server side.

Snowpark's architecture clearly distinguishes between client-side and server-side execution. Standard Python functions (like `math.sqrt`) executed in loops run on the client environment where the script is hosted, forcing data to be transferred out of Snowflake into local memory. Conversely, Snowpark functions (like `snowflake.snowpark.functions.sqrt`) translate into SQL and execute natively on the server side within Snowflake's highly scalable virtual warehouses, avoiding data movement.

Correct answer: True

This is True. While Snowflake partners with Anaconda to provide thousands of popular Python packages natively out-of-the-box (requiring no manual uploads), any custom or third-party packages not available in the Snowflake Anaconda channel must be manually uploaded to an internal stage and referenced using `session.add_import()` or the `@udf` decorator imports parameter.

Correct answer: Use Anaconda or Miniconda to create an environment, explicitly configuring the Snowflake Anaconda channel, and install packages from that channel.

To guarantee that the packages used during local development are exactly identical to what will be executed on the Snowflake server side, Snowflake strongly recommends using Anaconda (or Miniconda) and configuring the environment to pull exclusively from the Snowflake Anaconda channel (`conda create --name snowpark -c https://repo.anaconda.com/pkgs/snowflake snowflake-snowpark-python`). This prevents "works on my machine" versioning issues.

Correct answer: Microsoft Visual Studio Code (VS Code)

Microsoft Visual Studio Code (VS Code) is a local, third-party IDE that supports native Git integration, advanced step-through debugging, and offline code authoring. While Snowflake Notebooks and Snowsight Worksheets are excellent native Snowflake tools, they run entirely in the browser (requiring internet connectivity) and currently lack the deep local debugging and offline capabilities of a dedicated local IDE like VS Code. quadrantChart title Development Environment Comparison x-axis Browser-Based --> Local Desktop y-axis Basic Authoring --> Advanced Debugging quadrant-1 Specialized IDEs quadrant-2 Native UI quadrant-3 Legacy Tools quadrant-4 VS Code Snowsight Worksheets: [0.1, 0.4] Snowflake Notebooks: [0.2, 0.6] Jupyter Notebook (Local): [0.7, 0.5] VS Code: [0.9, 0.9]

Correct answer: private_key

When using Key Pair Authentication with the Snowpark API for Python, you must serialize the unencrypted private key into a binary format (DER bytes) and pass it to the connection dictionary using the `private_key` parameter. The Snowpark connector does not accept a direct file path (like `private_key_path`); the application code must read the file and pass the loaded key object.