Snowflake Certified SnowPro Specialty - Snowpark - SPS-C01 certification

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Snowflake SPS-C01 Exam Syllabus Topics:

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 'python-dotenv' package was installed, but the ' .env' file wasn't loaded by calling before creating the session.
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 required environment variables (e.g., 'SNOWFLAKE_USER, SNOWFLAKE_PASSWORD, 'SNOWFLAKE_ACCOUNT) are not defined or are incorrectly named in the ' .env' file.
D) 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.
E) The .env' file is not located in the same directory as the Python script.

2. You are developing a Snowpark stored procedure in Python that utilizes the 'requests' library to fetch data from an external API. Your Snowflake account is configured to use Anaconda packages. You encounter an error indicating that the 'requests' library is not found. Which of the following steps are MOST effective in ensuring the 'requests' library is available to your stored procedure?

A) Specify the 'requests library in the stored procedure's 'packages argument during creation: 'CREATE OR REPLACE PROCEDURE
B) Install the 'requestS library directly onto the Snowflake compute nodes using SnowSQL's command.
C) Enable Anaconda integration for your Snowflake account, ensuring 'requests' is available in the Snowflake Anaconda channel, and then create the stored procedure using 'imports=['snowflake://packages/requests/']'.
D) Include the 'requests' library directly in the stored procedure code using a base64 encoded string.
E) Manually upload the 'requests' library's ' .py' files to an internal stage and import them within the stored procedure.

3. Consider a scenario where you're developing a Snowpark stored procedure that accesses sensitive data'. Which of the following strategies, when used together, provide a comprehensive approach to secure this stored procedure and protect the underlying data?
Select all that apply:

A) Using 'EXECUTE AS OWNER and granting the 'SELECT privilege on the sensitive data tables to the stored procedure's owner role.
B) Masking sensitive data within the stored procedure using Snowflake's dynamic data masking policies.
C) Using 'EXECUTE AS CALLER and relying on the caller's privileges to access the data.
D) Implementing row-level security policies on the sensitive data tables.
E) Encrypting the stored procedure's code using AES encryption before deployment.

4. You are profiling a Snowpark application that uses a combination of SQL queries and Python UDFs. You observe that a particular stage involving a UDF is taking significantly longer than expected. You suspect that the UDF's performance is the bottleneck. Which of the following steps would be the MOST comprehensive approach to diagnose and address the performance issue?

A) Implement caching for the UDF's results to avoid recomputing the same values multiple times.
B) Increase the warehouse size and re-run the application. If the execution time improves significantly, the issue was resource contention.
C) Use Snowflake's query profile to examine the execution plan and identify the UDF-related stages with the highest execution time. Then, analyze the UDF's code for inefficiencies, such as unnecessary loops or complex calculations.
D) Convert the scalar UDF to a vectorized UDF, even without fully understanding the source of the performance bottleneck.
E) Replace the Python UDF with an equivalent SQL query using Snowflake's built-in functions. If the SQL query performs better, the Python UDF was the bottleneck.

5. You have a Snowpark DataFrame named 'employee_df with columns 'employee_id', 'department', and 'salary'. You want to calculate the average salary for each department and add it as a new column named 'avg_department_salary' to the original DataFrame. Additionally, you want to sort the resulting DataFrame by department and then by salary in descending order. Which of the following Snowpark code snippets correctly implements this requirement?

A)

B)

C)

D)

E)

Solutions: