Snowflake Certified SnowPro Specialty - Snowpark : SPS-C01

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. A data engineering team is building a Snowpark application in Python to perform advanced time series analysis on sensor data stored in Snowflake. They need to leverage a specific, but older, version of the 'pandas' library (version 1.1.5) that is not available in the default Snowflake Anaconda channel. Which of the following approaches is the MOST efficient and recommended way to ensure this specific version of 'pandas' is available to their Snowpark application, while minimizing security risks and operational overhead?

A) Package the pandas 1.1.5 library within the Snowpark application code itself, by including it in the application deployment package.
B) Use 'conda install pandas==l .1.5' within the Snowpark Python code directly. This will install pandas at runtime.
C) Upload the pandas 1.1.5 wheel file directly into a Snowflake stage and reference it within the session configuration using along with
D) Build a custom Anaconda channel outside of Snowflake, and configure the Snowpark session to point to that channel.
E) Create a new Snowflake Anaconda environment and install pandas 1.1.5 into this environment, then configure the Snowpark session to use this custom environment. Ensure the 'snowflake-snowpark-python' package is also included.

2. You are tasked with operationalizing a Snowpark Python UDF for batch scoring of a large dataset. The UDF takes a set of feature columns and returns a prediction. You want to optimize performance and resource utilization. Select all the strategies that would effectively improve the operational efficiency and scalability of your UDF execution.

A) Always use a warehouse size of 'X-Large' or larger regardless of the data volume to guarantee sufficient resources for UDF execution.
B) Adjust the 'MAX BATCH SIZE parameter for the warehouse executing the UDF to the largest possible value to minimize overhead.
C) Utilize the 'vectorized' argument during UDF registration to enable batch processing of input data within the UDF.
D) If the UDF performs external API calls, implement retry logic with exponential backoff to handle transient network errors gracefully.
E) Ensure that the Snowpark DataFrame being passed to the UDF is appropriately partitioned based on a relevant column (e.g., a geographical region) before invoking the UDF.

3. You have created a Snowpark UDF that uses a custom Python module 'my_module.py', containing a function 'process data'. This module is not available through Anaconda'. You've packaged the module into a zip file named 'my module.zip'. What steps are necessary to deploy this UDF in Snowflake so that it can correctly use the 'my_module'?

A) Upload 'my_module.zip' to an internal stage. When creating the UDF, specify the stage path in the 'packages' argument. Within the UDF, modify 'sys.path' to include the path where Snowflake unpacks the zip file.
B) Upload 'my_module.zip' to an internal stage. When creating the UDF, specify the stage path in the 'imports' argument. Within the UDF, modify 'sys.path' to include the path where Snowflake unpacks the zip file.
C) Upload 'my_module.zip' to an external stage (e.g., AWS S3 or Azure Blob Storage). Configure Snowflake to access the external stage. Create the UDF, specifying the external stage path in the 'imports' argument.
D) Upload 'my_module.zip' to an internal stage, then create the UDF using 'session.add_import' within the UDF definition, specifying the stage path. No additional configuration is needed.
E) Upload 'my_module.zip' to an internal stage. When creating the UDF, specify the stage path in the 'imports' argument. No changes to sys.path are required within the UDF.

4. You are developing a Snowpark application that requires secure access to Snowflake. You need to establish a session using Key Pair authentication. You have stored your private key in an encrypted file and have the passphrase available. Assume you've correctly configured your Snowflake user with the public key. Which of the following methods can be used to load the private key, assuming that 'PRIVATE KEY PATH' stores path to encrypted private key and 'PRIVATE KEY PASSPHRASE stores passphrase?

A)

B)

C)

D)

E)

5. You have a Python function that performs complex data transformations, too intricate to express directly in Snowpark SQL. You want to register this as a User-Defined Table Function (UDTF) so that it can be used to expand rows in a Snowpark DataFrame. The UDTF takes two arguments: an ID (integer) and a string. It returns a table with three columns: (integer), (string), and 'timestamp' (timestamp). Which of the following code snippets correctly registers this UDTF, making it available for use within Snowpark?

A)

B)

C)

D)

E)

Solutions: