Online Access Free SPS-C01 Practice Test

You've created a Snowpark Python UDF that uses a third-party library (e.g., scikit-learn) to perform machine learning inference. You need to ensure that this UDF is executed securely and efficiently in Snowflake. Which of the following approaches represent best practices for managing dependencies and securing the UDF environment? Select all that apply.

• A.Utilize Snowflake's managed dependencies to handle packages installation and versioning automatically. Register the UDF to run in a secure execution environment within Snowflake.
• B.Include the third-party library's code directly within the UDF's code.
• C.Disable security features to avoid dependency conflicts.
• D.Deploy the UDF as an external function and manage dependencies using the external function's environment.
• E.Use Snowflake's Anaconda channel to manage dependencies and specify the required packages in the UDF's registration using 'packages' argument.

You have a Snowpark DataFrame 'df that you want to persist as a Snowflake table. You need to ensure the following requirements are met: 1. The table should be created if it does not exist. 2. If the table exists, the new data should be merged with the existing data based on a 'primary_key' column. 3. If a row with a matching 'primary_key' already exists in the target table, update the existing row with the values from the 'df DataFrame. Otherwise, insert the row from 'df into the target table. Which of the following approaches can achieve this using Snowpark?

• A.Perform a full outer join between the existing table and the DataFrame, then use
• B.Use the method, specifying 'primary_key' in the options.
• C.Create a stored procedure that performs a MERGE statement in Snowflake, and call this stored procedure from your Snowpark code.
• D.There is no direct way to achieve a merge operation directly using Snowpark DataFrame API without either custom stored procedure or staging of dataframe to new table, then running merge query.
• E.Convert the Snowpark DataFrame to Pandas DataFrame, perform the merge operation using Pandas, and then write the Pandas DataFrame back to Snowflake.

You are building a Snowpark application to process sensitive data'. To enhance security, you want to leverage ephemeral sessions. Which configurations, passed to 'snowpark.Session.builder.configS , are required and sufficient to create an ephemeral session? Assume your Snowflake environment is properly configured to allow ephemeral sessions.

• A.Setting the 'role' parameter to a role with appropriate privileges, 'database', 'schema', and 'warehouse'.
• B.Setting the 'role' parameter to a role with appropriate privileges and 'warehouse'.
• C.Only setting the 'role' parameter to a role with appropriate privileges.
• D.Setting the 'role' parameter to a role with appropriate privileges and 'database' and 'schema'.
• E.Ephemeral sessions do not have specific configurations; they are automatically created by Snowflake when the application connects.

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 internal stage, then create the UDF using 'session.add_import' within the UDF definition, specifying the stage path. No additional configuration is needed.
• 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. When creating the UDF, specify the stage path in the 'imports' argument. No changes to sys.path are required within the UDF.

You are developing a Snowpark Python application to process streaming data from a Kafka topic, enrich it with data from a Snowflake table, and store the results in another Snowflake table. The enrichment process involves joining the streaming data with a large dimension table in Snowflake. Which of the following Snowpark features would be most efficient and scalable for this use case, considering the continuous nature of the streaming data and the size of the dimension table?

• A.Using a standard Snowpark DataFrame join operation with a broadcast hint on the smaller streaming data DataFrame.
• B.Leveraging a dynamic table to incrementally update the join of the streaming data and dimension table.
• C.Utilizing Snowpark UDFs to fetch data from the dimension table for each row of the streaming data DataFrame.
• D.Creating a Snowflake materialized view that pre-joins the streaming data (landing in a table) with the dimension table and using Snowpark to query the materialized view.
• E.Employing a Snowpark Stored Procedure that periodically refreshes a smaller, aggregated version of the dimension table and using that for joining with the streaming data.