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Consider a scenario where you have a table 'EMPLOYEES' with columns 'employee id', 'department', and 'salary'. You want to delete employees who belong to either the 'HR' or 'Finance' department and have a salary less than 60000. Which of the following Snowpark DataFrame operations correctly implements this deletion?

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

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Consider the following Snowpark Python code snippet that defines and applies a UDF:

Which of the following modifications would MOST likely improve the performance of this code, assuming the DataFrame 'df contains a large number of rows?

• A. Rewrite the 'apply_discount' function to use NumPy arrays internally for vectorized calculations, ensuring compatibility with vectorized UDF execution. The function signature will also need to accept arrays.
• B. Use and F.lit(0.2Y instead of 0.1 and 0.2 while creating the dataframe.
• C. Remove the 'input_types' argument from 'session.udf.register' . Snowflake can automatically infer the input types.
• D. Specify a different warehouse size when creating the Snowpark session using 'session = Session.builder.config('warehouse', 'XLARGE').configs(connection_params).create()'.
• E. Change to in the "session.udf.register' call, ensuring the function is updated to handle batches of data.

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A financial firm is using Snowpark Python to analyze stock trading data'. They have a DataFrame named 'trades' with columns 'trade_id', 'stock_symbol', 'trade_price', and 'trade_timestamp'. They want to identify potentially fraudulent trades based on the following criteria: 1. Trades where the 'trade_price' deviates significantly from the average price of that 'stock_symbol' over the past hour. 2. Trades originating from user accounts where the price is above $1000.3. Trades which has stock symbol 'XYZ'. The firm wants to apply multiple filters to the DataFrame to extract only the fraudulent trades and needs an efficient and concise approach using Snowpark. Which of the following code snippets, using 'trade_price' > 1000 as user identifier, MOST accurately and efficiently implements this filtering logic? Assume that a Snowflake user has a maximum amount they can spend on a trade, and therefore, the user ID is associated with 'trade_price'.

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

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You are developing a Snowpark application that requires secure access to Snowflake. You need to establish a session using Key Pair authentication. You have stored your private key in an encrypted file and have the passphrase available. Assume you've correctly configured your Snowflake user with the public key. Which of the following methods can be used to load the private key, assuming that 'PRIVATE KEY PATH' stores path to encrypted private key and 'PRIVATE KEY PASSPHRASE stores passphrase?

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

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You are using Snowpark for Python to process a large dataset of website clickstream data'. The dataset contains columns such as 'session_id', 'user_id', 'timestamp', 'page_url', and 'event_type' (e.g., 'click', 'pageview', 'purchase'). You want to identify fraudulent user sessions based on the following criteria: A user session is considered fraudulent if it contains more than 100 clicks within a I-minute window. A user session is considered fraudulent if it contains more than 5 purchase events within a 5-minute window. Which of the following code snippets demonstrates the most efficient way to identify fraudulent sessions using Snowpark for Python? Select two that apply.

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

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