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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Python dictionary 'data' representing configuration settings for your Snowpark application. You need to convert this dictionary into a Snowpark DataFrame with a single row and two columns named 'Setting' and 'Value'. The 'Setting' column should contain the keys from the dictionary, and the 'Value' column should contain the corresponding values. The DataFrame needs to be created efficiently and ensure string representation of both the setting and value. Which approach is most suitable, ensuring correctness and conciseness?

A) python import snowflake.snowpark.types as T settings = list(data.items()) schema = T.StructType([T.StructField('Setting', T.StringType()), T.StructField('Value', T.StringType())]) df = session.createDataFrame(settings, schema=schema)
B) 'python settings = [1k, v] for k, v in data.items()] df = session.createDataFrame(settings, schema=['Setting', 'Value'])
C) python settings = [1k, str(v)] for k, v in data.items()] schema = ['Setting', 'Value'] df = session.createDataFrame(settings, schema=schema)
D) python import pandas as pd pd_df = pd.DataFrame(data.items(), columns=['Setting', 'Value')) df = session.createDataFrame(pd_df)
E) 'python settings = [{'Setting': k, 'Value': v} for k, v in data.items()] df = session.createDataFrame(settings)

2. You have a complex data pipeline implemented using Snowpark Tasks in a Directed Acyclic Graph (DAG). One of the tasks, , depends on the successful completion of two parent tasks, and 'task B'. You need to implement error handling such that if 'task_R fails, 'task_C' should not be executed, but should still complete its execution regardless of status. If 'task B' fails, 'task_C' should not be executed. How do you configure the task dependencies and error handling in Snowflake to achieve this behavior?

A)

B)

C)

D)

E)

3. You are developing a Snowpark Python application and want to write unit tests for your custom UDFs using pytest. Consider the following UDF:

Which of the following pytest tests are correctly written and effectively test the UDF, including handling potential exceptions? (Select all that apply)

A)

B)

C)

D)

E)

4. You are building a Snowpark application that requires you to connect to Snowflake from an environment where directly specifying credentials in the code is not permitted for security reasons. Which of the following are valid and recommended ways to securely pass authentication information to the Snowpark Session?

A) Storing credentials in a dedicated secret management service (e.g., HashiCorp Vault, AWS Secrets Manager) and retrieving them using an appropriate API. This is the most secure and recommended approach for production environments.
B) Using the Snowflake CLI's 'snowflake configure' command and relying on the A.snowflake/config' file. This is suitable for development but not recommended for production due to local file dependency.
C) Storing credentials in a Snowflake stage and retrieving them from there at runtime. This is an acceptable, though more complex, solution.
D) Hardcoding the credentials in the Snowpark Python script and obfuscating them using Base64 encoding. This provides security by obscurity, making it a reasonably secure approach.
E) Using environment variables and retrieving them using 'os.environ' to build the connection parameters. This is a secure and recommended approach.

5. You are tasked with deploying a Snowpark Python application that utilizes a third-party library, 'scikit-learn' , for machine learning tasks. The application will be executed as a Snowflake Stored Procedure. What are the necessary steps to ensure the 'scikit-learn' library is available within the Snowpark environment?

A) Install scikit-learn on your local machine, package your snowpark code into a zip file and upload it to a stage, no extra steps are required.
B) Create a Snowflake Anaconda channel integration, add the 'scikit-learn' package to the channel, and then reference the channel in the Snowpark session configuration.
C) Upload the 'scikit-learrf library as a ZIP file to a Snowflake stage, create a Python UDF that unzips the library, and then import the library within the Snowpark Stored Procedure.
D) Include the 'scikit-learn' library directly in the Snowpark session using 'session.add_import(sklearny.
E) Create a Snowflake Anaconda environment using conda, include the 'scikit-learns package in the environment, and then create a Snowpark Stored Procedure that utilizes the environment via the 'packages' parameter in the CREATE PROCEDURE statement.

Solutions: