You have a Snowpark Python application that reads data from a Snowflake table named 'SALES DATA', performs several transformations using DataFrames, and then writes the results back to a new table named 'AGGREGATED SALES'. The application runs successfully, but you notice that the write operation to 'AGGREGATED SALES' is consistently slow. After examining the query profile, you observe significant skew in the data being written, causing some worker nodes to be overloaded. Which of the following techniques could you use within your Snowpark application to mitigate the data skew and improve the write performance to 'AGGREGATED SALES'?

• A. Use the 'DataFrame.sort(col)' method to sort the data by the skew key before writing it to 'AGGREGATED SALES'. This will ensure that rows with similar values are processed by the same worker node.
• B. Use the method to specify a clustering key on the 'AGGREGATED SALES' table during table creation. This will physically organize the data on disk based on the skew key, improving write performance.
• C. Increase the size of the Snowflake warehouse being used to execute the Snowpark application. This will provide more compute resources to handle the data skew.
• D. Implement custom partitioning logic using a User-Defined Function (UDF) that calculates a hash value based on the skew key and then uses the 'DataFrame.repartitionByRange(col)' method to partition the data based on the hash values.
• E. Use the method to evenly redistribute the data across a larger number of partitions before writing it to 'AGGREGATED SALES'.

You are developing a Snowpark stored procedure in Python to perform sentiment analysis on customer reviews. The procedure relies on a custom Python library, 'sentiment_analyzer.py' , which is not available in Snowflake's default Anaconda channel. You also need to include the 'nltk' library. Which of the following approaches is the MOST efficient and recommended way to make both dependencies available to your stored procedure within Snowflake?

• A. Install 'sentiment_analyzer.py' and 'nltk' on each Snowflake virtual warehouse node and set the 'PYTHONPATH' environment variable. (This will require contacting Snowflake support.)
• B. Create a Snowflake Anaconda channel package containing 'sentiment_analyzer.pV and 'nltk' using 'conda build' , then reference this package in your stored procedure's 'imports' parameter.
• C. Include the code from 'sentiment_analyzer.py' directly within the stored procedure's Python code and download 'nltk' modules from the internet each time the stored procedure is executed.
• D. Create a ZIP file containing 'sentiment_analyzer.py' and the required 'nltk' modules, upload it to a stage, and specify the stage path in the 'imports' parameter of the 'sproc' decorator.
• E. Upload 'sentiment_analyzer.py' and 'nltk"s compiled code as separate stages, then import them within the stored procedure using 'sys.path.append()'.

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 are tasked with creating a Snowpark DataFrame from a complex JSON structure stored in a VARIANT column named 'payload' within a table called 'events'. The 'payload' contains nested objects and arrays, and you need to extract specific fields into separate columns of the DataFrame. You need to extract the 'event_id' (INT) from the top level of the JSON, the 'user _ id' (INT) from the 'user' object nested within the 'payload' , and the first element of the 'tags' array (VARCHAR) also nested within the 'payload'. Which of the following code snippets correctly defines the schema using 'StructType' and 'StructField' and applies it during DataFrame creation assuming events table contains multiple rows?

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You have a Snowpark DataFrame 'df' containing customer data with columns 'customer id', 'name', 'age', and 'city'. You want to filter the DataFrame to include only customers from 'New York' who are older than 30, then extract the 'customer id' and 'name' into a Rows object, and finally print the 'name' of the first row in the Rows object. Which of the following code snippets correctly achieves this using Snowpark Python?

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