You are developing a Snowpark stored procedure to process PDF files stored in a Snowflake stage. You need to extract text from these PDF files and store the extracted text in a Snowflake table. Due to security requirements, you cannot use any external packages that require internet access. Which of the following approaches can you use to accomplish this task securely and efficiently? (Select all that apply)

• A. Use the function to read the PDF files as binary data. Implement a pure-Python PDF parsing library directly within the stored procedure to extract the text. Ensure the library code is included directly in the stored procedure code.
• B. Convert the PDF files to a text-based format (e.g., TXT) using an external tool before loading them into Snowflake. Then, use Snowpark to process the text files.
• C. Implement an external function using AWS Lambda or Azure Functions to parse the PDF files and extract the text. Configure the external function to have no internet access.
• D. Use Snowpark's built-in PDF parsing functions to extract the text. Snowflake provides native support for PDF parsing, eliminating the need for external libraries.
• E. Develop a custom Java UDF (User-Defined Function) that uses a secure, open-source PDF parsing library (e.g., PDFBox) and register it with Snowflake. Call this UDF from the Snowpark stored procedure to extract the text.

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You are developing a Snowpark application that uses a Python UDF to perform geocoding operations. This UDF relies on a third-party geocoding library and a large dataset of geographical data stored in a file named 'geodata.db'. The UDF needs to be operationalized with minimal latency. Which of the following strategies will result in the FASTEST execution of the UDF and optimal resource utilization?

• A. Create a Java UDF that performs the geocoding using a Java geocoding library. Upload the JAR file and 'geodata.db' to a stage and reference them using the 'imports' clause. Java UDFs always perform faster than Python UDFs.
• B. Package the geocoding library and 'geodata.db' file into a ZIP file. Upload the ZIP file to a Snowflake stage and reference it using 'imports' in the UDF definition. Use a virtual environment to manage package dependencies.
• C. Create a custom Anaconda channel containing the geocoding library and 'geodata.db'. Configure the Snowflake account to use this channel. No need to use virtual environment.
• D. Use an external function that calls a geocoding service over the internet. Store 'geodata.db' in an S3 bucket and access it from the external function. Call the external service whenever it requires it.
• E. Package the geocoding library and 'geodata.db' file into a ZIP file. Upload the ZIP file to a Snowflake stage and reference it using 'imports' in the UDF definition. Ensure 'geodata.db' is loaded only once into memory per worker process using global variable and proper caching for subsequent UDF invocations. Use a virtual environment to manage package dependencies.

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You have two Snowpark DataFrames, 'dfl' and 'df2 , both containing customer data, but with slightly different schemas. 'dfl' has columns 'customer_id', 'name', and 'email'. 'df2' has columns 'id', 'customer name', and 'email_address'. You want to perform a set- based operation to find all unique customer IDs present in 'dfl but NOT in 'df2' , considering that 'customer_id' in 'dfl corresponds to 'id' in 'df2. Which of the following code snippets will achieve this, ensuring that column names are correctly aligned before the operation?

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

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A Snowpark application needs to dynamically switch between different Snowflake accounts based on the environment (development, staging, production). Which of the following approaches provides the MOST secure and maintainable way to manage account credentials without hardcoding them in the application? Assume that deployment will occur via docker, Kubernetes or other modern deployment practices.

• A. Hardcode credentials in the Snowpark application code and rely on network security to prevent unauthorized access.
• B. Store credentials in separate .env' files for each environment and load the appropriate file based on an environment variable indicating the current environment.
• C. Store credentials in environment variables managed by the deployment platform (e.g., Kubernetes secrets) and access them using
• D. Use the Snowflake CLI configuration file ('-/.snowflake/config') and switch between named profiles based on an environment variable.
• E. Encrypt the credentials and store them in a configuration file that is decrypted at runtime using a key stored in a secure vault.

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You have a Snowpark DataFrame 'sales df with columns 'product_id' (INTEGER), 'sale date' (DATE), and 'sale_amount' (DOUBLE). You need to filter the DataFrame to include only sales that occurred in the year 2023 and where the 'sale_amount' is greater than the average 'sale_amount' for that specific 'product id'. Which of the following Snowpark Python code snippets will correctly achieve this?

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

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