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Snowflake Certified SnowPro Specialty - Snowpark Sample Questions:

1. You have a Snowpark Python stored procedure named 'calculate_stats' that takes a table name as input and returns summary statistics. You need to modify the stored procedure to add a new optional parameter for specifying a filter condition. Which of the following SQL commands, used in conjunction with the Snowpark API for Python, is the MOST efficient way to alter the existing stored procedure without dropping and recreating it?

A)

B)

C)

D)

E)

2. You are tasked with building a Snowpark Python application to process JSON files stored in a Snowflake stage. The JSON files contain customer feedback data, including sentiment scores. You need to create a stored procedure that reads the JSON files, calculates the average sentiment score, and stores the result in a Snowflake table. You also need to handle potential errors, such as invalid JSON format in some files, and continue processing other files. Which of the following approaches is MOST efficient and robust to handle this scenario?

A) Implement an external function using AWS Lambda or Azure Functions to parse the JSON files and calculate the average sentiment score. Call this external function from the stored procedure. This provides better error handling and scalability.
B) Download the JSON files to the Snowpark client, process them using standard Python libraries (like 'json'), and then upload the aggregated data back to Snowflake using 'session.write_pandas()'. Handle errors locally and log them.
C) Use and return a DataFrame containing the average sentiment score and file name for each processed file. Handle JSON parsing errors by skipping the file and logging the error. Use to write the DataFrame to the target table.
D) Load the JSON data into a VARIANT column in a temporary table. Use a Snowpark DataFrame transformation to parse the JSON data from the VARIANT column. Catch errors during the DataFrame transformation process and log them to a separate table. Finally, calculate the average sentiment score using Snowpark functions.
E) Use the function directly within the stored procedure to parse each JSON file. Catch exceptions within the loop and log errors to a separate table. Use 'DataFrame.write.mode('append')' to write the average sentiment score to the target table.

3. You have a DataFrame 'df containing user profile data'. A column named "profile" stores JSON objects with potentially missing fields. These objects might include 'name', 'age', 'location', and 'preferences'. You need to extract the user's name and age. If 'age' is missing, you want to default to 0. Furthermore, you want to filter out any rows where the 'location' field is an empty string. Which combination of Snowpark code snippets will achieve this? (Select all that apply)

A)

B)

C)

D)

E)

4. A data engineering team wants to create a Snowpark stored procedure that takes a VARIANT column from a Snowflake table, parses a specific JSON element within each row, and returns a new DataFrame with the extracted data as a STRING column. The JSON structure is consistent across all rows. What is the MOST efficient and type-safe way to implement this, considering the need for performance and maintainability?

A) Use Snowpark's 'get' function within the stored procedure to extract the JSON element, explicitly cast the extracted value to STRING using 'cast('string')' , and register the stored procedure with defining the output schema.
B) Use Python type hints for the input VARIANT column, extract the JSON element using string manipulation within the stored procedure, and return a DataFrame with the extracted data as a string.
C) Define the input column as a generic 'object' type in Python, use Snowpark's 'get function with path navigation to extract the JSON element, and return the extracted data as a string using 'as_varchar'.
D) Define the input column using and use the operator to implicitly convert the extracted JSON element to a string, relying on Snowpark's type inference for the return type.
E) Use the 'get' function on the VARIANT column to extract the JSON element, use the 'as_varchar' function to cast the VARIANT value to a String value, and register the stored procedure with explicit 'return_type' and schema definition for enhanced type safety

5. You are developing a Snowpark application to process customer sentiment from text reviews. You have a Python function, , that utilizes a pre-trained NLP model loaded from a file on a Snowflake stage named This function returns a sentiment score (float) between -1 and 1. You need to register this function as a UDF so that it can be used within Snowpark DataFrames. Which of the following code snippets correctly registers the UDF, ensuring the NLP model is available to the function during execution?

A)

B)

C)

D)

E)

Solutions: