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

You need to perform a set difference operation between two DataFrames in Snowpark Python. 'dfl' contains customer IDs from a marketing campaign, and 'df2 contains customer IDs from a recent purchase event. You want to identify customers who were targeted in the campaign but did not make a recent purchase. Both DataFrames have a column named 'customer id'. Which of the following approaches provides the most efficient way to accomplish this task in Snowpark?

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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?

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

You are working with a Snowpark DataFrame containing employee data, including columns 'employee_id', 'first_name', 'last_name', 'salary', and 'department'. You need to perform the following transformations: 1. Concatenate 'first_name' and into a new column called separating them with a space. 2. Increase each employee's salary by a percentage based on their 'department'. Department 'Sales' gets a 10% raise, 'Marketing' gets a 15% raise, and all other departments get a 5% raise. 3. Create a new column reflecting this raise. Which of the following Snowpark code snippets achieves these transformations correctly and efficiently? (Select all that apply)

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You have a Snowpark DataFrame containing semi-structured data in a column named 'payload'. The 'payload' column contains JSON objects, and some of these objects contain nested arrays. You need to flatten all arrays, regardless of their level of nesting, and extract specific fields from the flattened data'. What is the MOST efficient approach using Snowpark to achieve this while minimizing the amount of code?

• A. Create a stored procedure in Snowflake that recursively flattens the JSON, then call this stored procedure from Snowpark to transform the DataFrame.
• B. Iteratively apply the 'explode' function to each array field within the 'payload' column, manually identifying and flattening each level of nesting.
• C. Convert the DataFrame to an RDD, then use the RDD's 'flatMap' function to flatten the nested arrays before converting back to a DataFrame.
• D. Use a single ' SELECT statement with multiple 'LATERAL FLATTEN' calls (using SQL syntax within 'session.sql') to flatten all nested arrays simultaneously.
• E. Use recursive UDFs to traverse and flatten the JSON structure, then create a new DataFrame from the flattened data.