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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You are tasked with setting up Snowpark sessions using environment variables defined in a .env' file. You have successfully installed the 'python-dotenv' package and configured your .env' file with the necessary Snowflake connection parameters. However, when your Snowpark application attempts to create a session, it fails with a connection error. Which of the following could be the possible reasons for the failure, assuming you are correctly using 'os.getenv' to access the environment variables?

A) The required environment variables (e.g., 'SNOWFLAKE_USER, SNOWFLAKE_PASSWORD, 'SNOWFLAKE_ACCOUNT) are not defined or are incorrectly named in the ' .env' file.
B) The warehouse defined in your session creation code does not exist or the role defined in the 'snowflake.connector.connect' does not have appropriate warehouse privileges.
C) The Snowflake account identifier specified in the ' .env' file is incorrect or inaccessible from the network where the Snowpark application is running.
D) The 'python-dotenv' package was installed, but the ' .env' file wasn't loaded by calling before creating the session.
E) The .env' file is not located in the same directory as the Python script.

2. You have a Snowpark Python stored procedure that needs to access environment variables stored securely within Snowflake. Which of the following code snippets demonstrates the correct way to retrieve the value of an environment variable named 'API KEY' within your stored procedure?

A)

B)

C)

D)

E)

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. No changes to sys.path are required within the UDF.
C) 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.
D) 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.
E) 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.

4. A Snowpark application needs to dynamically switch between different Snowflake accounts based on the environment (development, staging, production). Which of the following approaches provides the MOST secure and maintainable way to manage account credentials without hardcoding them in the application? Assume that deployment will occur via docker, Kubernetes or other modern deployment practices.

A) Encrypt the credentials and store them in a configuration file that is decrypted at runtime using a key stored in a secure vault.
B) Use the Snowflake CLI configuration file ('-/.snowflake/config') and switch between named profiles based on an environment variable.
C) Store credentials in environment variables managed by the deployment platform (e.g., Kubernetes secrets) and access them using
D) Hardcode credentials in the Snowpark application code and rely on network security to prevent unauthorized access.
E) Store credentials in separate .env' files for each environment and load the appropriate file based on an environment variable indicating the current environment.

5. You have a complex Snowpark Python UDF that aggregates data from various sources and returns a dictionary containing several metrics (e.g., '{'average price': 12.50, 'total sales': 1000, 'customer count': 50}'). You need to operationalize this UDF and ensure proper data type handling for each metric. Which of the following is the MOST appropriate way to define the return type using the registration API?

A) Use a single 'ArrayType' to represent the entire dictionary. 'Integer Type').
B) Use a 'MapType' with 'StringType' as the key type and 'VariantType' as the value type.
C) Define a 'StructType' with ' StructFielcf for each metric, specifying the appropriate data type (e.g.,
D) Define the return type as 'StringType' and serialize the dictionary to JSON within the UDF.
E) Use a single 'VariantType' to represent the entire dictionary.

Solutions: