SPS-C01 試験問題を無料オンラインアクセス

You have a Snowpark DataFrame named and want to create a stored procedure that calculates the average purchase amount for each customer. The stored procedure should accept the DataFrame as input, perform the aggregation, and return a new DataFrame with the results. Which of the following code snippets BEST demonstrates how to correctly define and deploy this stored procedure?

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You are working with a Snowpark DataFrame that contains product information including 'product_name' and 'description'. You need to create a new column named 'search_terms' that contains the first three words from the 'description' column, converted to lowercase. If the description has fewer than three words, the 'search_terms' column should contain all the words available. The words should be separated by a space. What is the MOST efficient way to achieve this using Snowpark?

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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.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.
• B.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.
• C.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.
• 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.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.

A Snowpark application needs to dynamically switch between different Snowflake accounts based on the environment (development, staging, production). Which of the following approaches provides the MOST secure and maintainable way to manage account credentials without hardcoding them in the application? Assume that deployment will occur via docker, Kubernetes or other modern deployment practices.

• A.Store credentials in separate .env' files for each environment and load the appropriate file based on an environment variable indicating the current environment.
• B.Store credentials in environment variables managed by the deployment platform (e.g., Kubernetes secrets) and access them using
• C.Encrypt the credentials and store them in a configuration file that is decrypted at runtime using a key stored in a secure vault.
• D.Use the Snowflake CLI configuration file ('-/.snowflake/config') and switch between named profiles based on an environment variable.
• E.Hardcode credentials in the Snowpark application code and rely on network security to prevent unauthorized access.

You are tasked with creating a Snowpark UDTF (User-Defined Table Function) in Python to process a large CSV file stored in a Snowflake stage. Each row in the CSV represents a transaction, and you need to parse each row and extract specific fields based on a complex set of rules. The UDTF should return a table with the extracted fields. Consider the following code snippet:

• A.The UDTF will execute correctly and efficiently in Snowpark, correctly processing each row of the CSV and returning the extracted fields as a table.
• B.The UDTF will run, but it will be slow due to the use of pandas DataFrame operations within the UDTF. Consider optimizing the code to use Snowpark DataFrame operations instead.
• C.The UDTF will run but will not return any data since the code currently lacks a 'session' object properly initialized for Snowpark operations inside the handler. Ensure the handler method has the session parameter and uses it.
• D.The UDTF will fail because the 'yield' statement is being called after using 'return' in the processing block. Remove the yield statement as it is incompatible.
• E.The code will raise an error because the 'read_csvs function is not available within the Snowpark UDTF context. The input needs to be processed differently.