You are using Snowpark to process a DataFrame 'employee df containing employee data, including 'employee_id', 'name' , 'department' , and 'salary'. You need to implement a complex data cleaning and transformation pipeline that involves the following steps: 1. Remove duplicate rows based on 'employee id'. 2. Fill missing 'salary' values with the average salary for the employee's department. 3. Standardize department names by converting them to uppercase. 4. Create a new column 'salary_range' based on the salary. if Salary less than 50k 'Low', greater than 50k and less than 100k 'Medium', greater than 100k 'High'. Which of the following code snippets MOST effectively combines these transformations into a single, readable, and efficient Snowpark pipeline? Assume you have a session object available named 'session' and import necessary modules from 'snowflake.snowpark.functions as F'

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You are tasked with optimizing a Snowpark Python application that performs complex geospatial calculations on a large dataset. The application experiences significant performance bottlenecks due to the computational intensity of the geospatial functions. Which of the following strategies would be MOST effective in improving performance?

• A. Distribute the dataset into smaller chunks using partitioning strategies within the Snowpark DataFrame API and process them independently.
• B. Disable automatic query optimization features in Snowflake to gain more control over query execution.
• C. Utilize user-defined functions (UDFs) written in Java or Scala and leverage vectorized UDFs where possible.
• D. Rewrite the geospatial functions using native Python libraries within the Snowpark environment.
• E. Increase the size of the virtual warehouse to a larger instance (e.g., from X-SMALL to LARGE).

You have two Snowpark DataFrames, 'dfl' and 'df2, representing customer data'. 'dfl' contains columns 'CUSTOMER ID', 'NAME, and 'EMAIL', while 'df2 contains 'CUSTOMER ID' and 'PURCHASE AMOUNT'. You need to create a new DataFrame that combines the information from both DataFrames but only includes customers who exist in BOTH 'dfl ' and 'df2 and the resulting DataFrame should have columns from both. Which of the following Snowpark DataFrame operations should you use, and what is the correct way to call it?

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You have a Snowpark DataFrame containing sales data with columns 'sale_date', and 'sale_amount'. You need to calculate the cumulative sales amount for each product over time, ordered by 'sale_date'. Which of the following Snowpark code snippets correctly implements this using window functions?

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