Snowflake SPS-C01 Exam : Snowflake Certified SnowPro Specialty - Snowpark

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Snowflake SPS-C01 Exam Syllabus Topics:

Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

Question 1

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. Manually upload the 'requests' library's ' .py' files to an internal stage and import them within the stored procedure.
B. Install the 'requestS library directly onto the Snowflake compute nodes using SnowSQL's command.
C. Specify the 'requests library in the stored procedure's 'packages argument during creation: 'CREATE OR REPLACE PROCEDURE
D. 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/']'.
E. Include the 'requests' library directly in the stored procedure code using a base64 encoded string.

Question 2

You are working with sensor data in Snowpark. Your data contains (Integer), 'timestamp' (Timestamp), 'temperature' (Double), and 'status' (String). You need to create a Snowpark DataFrame named representing this data'. Which of the following is the most efficient and type-safe way to create the DataFrame from a list of Python tuples using an explicitly defined schema, assuming you need to maintain maximum precision for temperature readings and that all data types should map to the most efficient and appropriate Snowflake data type?

A.

B.

C.

D.

E.

Question 3

You have configured your Snowpark application to use a '.env' file for storing connection parameters. The ' .env' file contains the following:

Which of the following code snippets demonstrates the most secure and recommended method for creating a Snowpark session using these environment variables and also ensuring the file exists?

A.

B.

C.

D.

E.

Question 4

You have a Snowpark DataFrame named with the following schema: 'product_id' (INTEGER), (STRING), 'category' (STRING), 'price' (FLOAT), and 'description' (STRING). You want to perform several data cleaning and transformation steps. Which of the following operations can be efficiently chained together using Snowpark DataFrames to clean null values in 'description', replace special characters in 'product_name' and standardize 'category' values? Select all that apply:

A. Using the method to replace null values in the 'description' column with a default string 'No description available'.
B. Using the 'coalesce' function to fill null values in 'description' with values from a separate 'backup_description' column (if available).
C. Using the function to remove special characters (e.g., '$', '#, '@') from the 'product_name' column using a regular expression.
D. Manually iterating through each row of the DataFrame and applying Python string manipulation functions to clean the data. (e.g. row['description'] =
E. Using a UDF (User-Defined Function) written in Python to standardize the 'category' column by converting all values to lowercase and removing leading/trailing spaces.

Question 5

You are working with a Snowpark DataFrame containing customer data'. One of the columns, 'phone number', contains phone numbers in various formats (e.g., '123-456-7890', '(123) 456-7890', '1234567890'). You need to standardize all phone numbers to the format '+1-123-456-7890' using Snowpark for Python. You also want to handle cases where the phone number is NULL gracefully, replacing them with '+1-000-000-0000'. Which of the following Snowpark code snippets is the most efficient and correct way to achieve this?

A.

B.

C.

D.

E.

Solutions: