SPS-C01 Sample Questions & Answers
Filtering, cleaning and aggregating data through Snowpark takes the most weight, next to Python-based sessions and DataFrames built for unstructured content, basic architecture and setup, and configuring Snowpark-optimized warehouses for better performance.
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- Question 1Beginner
Snowpark Concepts · Outline Snowpark architecture
True or False: Third-party Python packages that are not managed by the Snowflake Anaconda repository must be uploaded to a Snowflake stage and added via
session.add_import()to be utilized within Snowpark server-side objects like UDFs.Show answer & explanation
Correct answer: A
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@udfdecorator imports parameter. - Question 2Intermediate
Snowpark Concepts · Set-up Snowpark
A developer is configuring their local development environment to build a Snowpark Python application. They want to ensure absolute compatibility with the packages that will eventually run server-side inside Snowflake's execution environment. Which installation approach is considered the best practice by Snowflake for setting up the Python environment?
Show answer & explanation
Correct answer: D
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. - Question 3Intermediate
Snowpark Concepts · Set-up Snowpark
A data science team is evaluating development environments for a new ML pipeline using Snowpark. They need an environment that provides native Git integration, advanced debugging, and the ability to work entirely offline for local code authoring before pushing to Snowflake. Which development environment best satisfies these specific requirements?
Show answer & explanation
Correct answer: C
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] - Question 4Intermediate
Snowpark API for Python · Create and manage user sessions
A security architect is configuring a headless automated service to connect to Snowflake using Snowpark for Python. To adhere to corporate security policies, passwords cannot be utilized. The architect decides to use Key Pair Authentication. Which parameter must be passed to the
Session.builder.configs()dictionary to supply the private key?Show answer & explanation
Correct answer: C
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_keyparameter. The Snowpark connector does not accept a direct file path (likeprivate_key_path); the application code must read the file and pass the loaded key object. - Question 5Beginner
Snowpark API for Python · Create and manage user sessions
A developer is writing a Python script to establish a Snowpark session. They have defined their connection parameters in a dictionary named
connection_parameters. Which syntax correctly instantiates the Snowpark session using the SessionBuilder?Show answer & explanation
Correct answer: C
The correct pattern to instantiate a session using the Snowpark API for Python is to use the
Session.builderobject, pass the dictionary to the.configs()method, and then invoke.create(). The exact syntax isSession.builder.configs(connection_parameters).create(). - Question 6Advanced
Snowpark API for Python · Create and manage user sessions
A data engineer is utilizing Snowpark to execute a long-running DataFrame transformation that typically takes 45 minutes to complete. The engineer wants the Python script to issue the command and immediately continue executing subsequent local Python code without waiting for the Snowflake query to finish. Which execution approach should the engineer implement?
Show answer & explanation
Correct answer: A
In Snowpark Python, you can execute queries asynchronously by passing
block=Falseto action methods like.collect(),.save_as_table(), or by calling methods on the.async_property. This immediately returns anAsyncJobobject, freeing the Python thread to continue. The status of the execution can later be checked using theAsyncJobobject.
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