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. Consider the following Python code snippet using Snowpark:

Which of the following statements are true regarding this Snowpark code?

A) The code will fail because password authentication is deprecated and replaced by Key Pair authentication.
B) The code reads data from a table named 'my_table' in Snowflake.
C) The code calculates the sum of the 'sales' column, grouped by 'category' , and saves the result to a new table named , overwriting it if it exists.
D) The code uses best practices by explicitly closing the Snowflake session, preventing resource leaks.
E) The code establishes a connection to Snowflake using the provided credentials.

2. You are working with a data science team that needs to create Snowpark DataFrames from various file types (CSV, JSON, Parquet, and XML) stored in different locations (internal stages, external stages on AWS S3, and Azure Blob Storage). The team wants a unified and reusable function to create DataFrames, abstracting away the specific file format and location details. Which of the following approaches using Snowpark Python API will provide the MOST flexible and maintainable solution?

A) Create a class hierarchy with an abstract base class 'DataFrameReader' that defines a 'read_file' method. Implement subclasses for each file format and location, overriding the 'read_file' method with the specific logic for that format and location.
B) Create separate functions for each file type and location combination (e.g.,
C) Create a generic function str, file_format: str, options: dicty that uses 'getattr(session.read, file format)' to dynamically call the appropriate 'session.read' method based on the 'file_format' parameter. Pass additional configuration through the 'options' dictionary.
D) Use the 'session.sqr method with dynamically generated SQL queries that include the file format and location details. Construct the SQL query string based on the input parameters.
E) Implement a single function that uses a series of 'if/elif/else' statements to determine the file type and location, then calls the appropriate 'session.read' method with the corresponding options.

3. You've written a Snowpark stored procedure in Python that utilizes a custom Python library named 'my utils.py' containing utility functions for data transformation. This library is not available in Anaconda'. You want to deploy the stored procedure and ensure that the custom library is accessible during execution. Which is the most appropriate way to operationalize your Snowpark stored procedure with the custom library?

A) Install 'my_utils.py' on the Snowflake warehouse where the stored procedure will be executed.
B) Package 'my_utils.py' into a wheel file and upload it to a Snowflake stage. Then, add the stage path to the 'packages' argument of the 'session.sproc.register' method.
C) Create a Snowpark DataFrame from 'my_utils.py' and pass it as an argument to the stored procedure.
D) Upload 'my_utils.py' to a Snowflake stage and specify the stage path in the 'imports' argument of the 'session.sproc.register' method.
E) Copy the contents of 'my_utils.py' directly into the stored procedure's code.

4. 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 Snowflake account identifier specified in the ' .env' file is incorrect or inaccessible from the network where the Snowpark application is running.
C) The 'python-dotenv' package was installed, but the ' .env' file wasn't loaded by calling before creating the session.
D) The .env' file is not located in the same directory as the Python script.
E) 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.

5. You have two Snowpark DataFrames, 'dfl' and 'df2', representing customer data'. 'dfl' contains customer IDs and names, while 'df2' contains customer IDs and email addresses. You need to create a new DataFrame that contains all customer IDs, names, and email addresses, including customers present in only one of the DataFrames. Which Snowpark set operation and join type would be most appropriate for achieving this?

A) DISTINCT and CROSS JOIN
B) UNION ALL and INNER JOIN
C) UNION and FULL OUTER JOIN
D) INTERSECT and LEFT JOIN
E) EXCEPT and RIGHT JOIN

Solutions: