You are working with a Snowpark DataFrame named 'employees_df' that contains employee data, including a column named 'hire_date' of type String. The date format in the 'hire_date' column is 'MM/DD/YYYY'. You need to convert this column to a DateType so you can perform date-based calculations. Which of the following Snowpark code snippets correctly converts the column to a DateType?

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

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You are using Snowpark Python to create a DataFrame from an existing Snowflake table "SALES DATA'. You want to apply a user- defined function (UDF) to each row of the DataFrame to calculate a custom sales metric. The UDF requires access to the 'session' object. Which of the following approaches is correct for defining and applying the UDF in Snowpark?

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

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You are using Snowpark Python to build a data pipeline. You need to version control your Snowpark application and ensure that it is compatible with different Snowflake environments (development, staging, production). Which strategies and tools would be most effective for managing the Snowpark application's code, dependencies, and deployment process?

• A. Use a Git repository to manage the Snowpark Python code, a dependency management tool like Poetry or pip to handle dependencies, and a CI/CD pipeline (e.g., using Jenkins or GitLab CI) to automate deployment to different Snowflake environments.
• B. Store the Python code directly in Snowflake stages and use Snowflake's versioning capabilities to manage different versions.
• C. Copy and paste the Python code between different Snowflake environments as needed, manually installing any required dependencies.
• D. Package all Snowpark code into a single ZIP file and manually upload it to each environment.
• E. Rely solely on Snowflake's built-in Python interpreter and avoid using any external libraries or dependencies to simplify versioning and deployment.

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You have a Snowflake table named 'CUSTOMER DATA' with columns 'CUSTOMER ID', 'NAME, 'CITY , and 'ORDER COUNT. You want to create a Snowpark DataFrame named 'customer_df containing only customers from 'New York' with an 'ORDER COUNT greater than 10. Which of the following code snippets is the MOST efficient and correct way to achieve this, minimizing data transfer and maximizing pushdown optimization to Snowflake?

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

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You are tasked with deploying a set of Python UDFs and UDTFs to a Snowflake environment using Snowpark. These functions rely on several external Python packages and need to be versioned and managed effectively. Which of the following strategies provides the MOST robust and scalable solution for managing dependencies and deploying these functions in a reproducible manner?

• A. Options A,B and C are all equally viable options
• B. Creating a 'requirements.txt' file, using 'conda' to create an environment.yml and use 'snowflake.snowpark.functions.udf and 'session.udtf.register' with the environment.yml. Update the environment.yml manually when dependencies are upgraded.
• C. Manually uploading the required Python packages to a Snowflake stage and specifying them in the 'packages' argument of the '@sf.UDF and 'session.udtf.register' calls. Update the packages on stage every time dependencies are upgraded.
• D. Creating a conda environment specification file (environment.yml) that lists all dependencies, storing the environment.yml file in a Snowflake stage. Update the environment.yml when dependencies are upgraded.
• E. Creating a 'requirements.txt' file and including all required packages in it. Zipping this file and uploading it to a Snowflake stage, and specifying it in 'imports'.

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