You're developing a Snowpark Python application that reads data from a Snowflake table, performs several transformations, and writes the result to another table. You notice that the application throws a 'net.snowflake.snowpark.exceptions.SnowparkClientException: JDBC driver encountered an unexpected error.' intermittently. Examining the Snowflake query history, you observe many queries failing due to 'Warehouse Suspended'. Which of the following strategies would BEST address this issue in a production environment?

• A. Increase the AUTO SUSPEND parameter of the warehouse to a larger value, preventing it from suspending so quickly.
• B. Schedule the Snowpark application to run during off-peak hours when warehouse load is lower and less likely to be suspended.
• C. Use a larger warehouse size to prevent warehouse overload during query execution, which might trigger the auto-suspension.
• D. Implement retry logic within the Snowpark application using a try-except block and exponential backoff to handle transient connection errors.
• E. Modify the Snowpark code to explicitly resume the warehouse at the beginning of the application execution and suspend it at the end.

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A data engineering team is using Snowpark Python to build a data pipeline. They need to create a User-Defined Function (UDF) that transforms a JSON string column representing customer information into a STRUCT type containing flattened fields for 'name', 'age', and 'city'. The UDF should handle null values gracefully and return NULL if the input JSON is invalid or if the 'name' field is missing. Considering performance implications and error handling, which of the following approaches is MOST optimal for defining and registering this UDF?

• A. Using 'snowflake.snowpark.functions.sproc' to create a stored procedure that performs the JSON transformation and returns the transformed data.
• B. Using 'session.register_function' to register a Python function as a UDF with and manually constructing a VARIANT object in Python from the extracted JSON fields.
• C. Using 'snowflake.snowpark.functions.udf with defining the STRUCT schema explicitly, and handling JSON parsing and field extraction using the 'snowflake.snowpark.functions.parse_json' function. Return None for invalid json.
• D. Using 'snowflake.snowpark.functions.udf with and relying solely on Snowflake's built-in JSON functions within the UDF, even for complex transformations, and handling exceptions with try-except blocks within the UDF to return NULL.
• E. Using 'snowflake.snowpark.functions.udf with and handling JSON parsing and field extraction using standard Python libraries within the UDF, returning a JSON string representation of the STRUCT.

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You are developing a Snowpark application to ingest a large dataset into Snowflake. You have a DataFrame with a schema that matches the target table 'TARGET TABLE. Due to network constraints, you need to optimize the insertion process to minimize the number of API calls. Which of the following approaches would provide the MOST efficient way to insert the data?

• A. Load the DataFrame into chunks of 1000 records into target table by using insert_into() function iteratively
• B. Stage 'data_df to an internal stage, then load the data from stage to the table using COPY INTO command.
• C. Directly use the method, without any intermediate transformations.
• D. Convert 'data_df to a Pandas DataFrame using and then use to insert the data.
• E. Iterate through the rows of 'data_df , constructing and executing a separate INSERT statement for each row using 'session.sql()'.

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You are working with a Snowpark DataFrame containing customer data, including a 'phone_number' column. Some phone numbers are missing or have incorrect formats. You want to impute missing values with a default phone number '000-000-0000' and remove any phone numbers that do not match the pattern 'XXX-XXX-XXXX' using Snowpark Python. Which of the following code snippets achieves this most efficiently?

• A.
  
• B.
  
• C.
  
• D.
  
• E.
  

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You have a Snowpark DataFrame containing customer data'. You need to create a stored procedure that accepts the DataFrame and a list of column names as input and returns a new DataFrame containing only the specified columns. Which of the following approaches correctly implement this functionality and handles data types effectively (Select all that apply)?

• A.
  
• B.
  
• C.
  
• D.
  
• E.
  

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