You're designing a data pipeline in Snowflake that utilizes an external function to perform sentiment analysis on customer reviews using a third-party NLP service. This service charges per request. You need to minimize costs while ensuring timely processing of the reviews.
Which of the following strategies would be most effective in optimizing the cost and performance of your external function?

• A. Implement a caching mechanism (e.g., using a Snowflake table or an external cache) to store the sentiment analysis results for frequently occurring reviews or similar text patterns, avoiding redundant API calls.
• B. Implement rate limiting and error handling in the external service (e.g., AWS Lambda or Azure Function) to gracefully handle API usage limits and prevent excessive charges due to errors.
• C. Set 'MAX BATCH_ROWS' to a very high value (e.g., 10000) to maximize the number of rows processed per API call, even if it increases latency for individual reviews.
• D. Bypass the external function completely and rely solely on Snowflake's built-in NLP functions for sentiment analysis.
• E. Pre-process the customer reviews in Snowflake to filter out irrelevant reviews (e.g., very short reviews or reviews with stop words) before sending them to the external function.

You have a requirement to create a UDF in Snowflake that transforms data based on a complex set of rules defined in an external Python library. The library requires specific dependencies. You also need to ensure the UDF is secure and that the code is not visible to unauthorized users. Which of the following steps MUST be taken to achieve this?

• A. Package all the Python libaries code into one file, then create an Javascript UDF and load/execute the python code inside the Javascript UDF.
• B. Upload the Python library and its dependencies as internal stages. Create a Java UDF that executes the Python code using the 'ProcessBuilder' class. Mark the Java UDF as 'SECURE'
• C. Create an external function pointing to an AWS Lambda function or Azure Function that hosts the Python code and its dependencies. Secure the external function using API integration and role-based access control.
• D. Create a Snowflake Anaconda environment specifying the required Python library dependencies. Then, create a Python UDF, reference the Anaconda environment, and use the 'SECURE' keyword.
• E. Create a Python UDF and directly upload the Python library code into the UDF's body. Snowflake automatically manages dependencies for UDFs.

You have a table named 'ORDERS' with a column 'ORDER DETAILS' that contains JSON data'. You want to extract a specific nested value ('customer id') from this JSON data using a SQL UDE The JSON structure varies, and sometimes the 'customer id' field might be missing. You need to create a UDF that handles missing fields gracefully and returns NULL if 'customer id' is not found. Also, You are looking for a performant solution that is highly scalable. Which of the following SQL UDF definitions is most appropriate?

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

You have a Snowflake table named 'ORDERS' with columns 'ORDER D', 'CUSTOMER D', and 'ORDER JSON' (a variant column storing order details). You need to extract specific product names from the 'ORDER JSON' column for each order and return them as a table. The 'ORDER JSON' structure is an array of objects, where each object represents a product with fields like 'product_name' and 'quantity'. Which approach is the most efficient and scalable way to achieve this, considering the possibility of millions of rows in the 'ORDERS table?

• A. Create a Java UDF that takes ORDER JSON' as input, parses it using a JSON library, extracts the product names, and returns a comma-separated string. Use a WHILE loop within the UDF to parse the JSON array.
• B. Create a JavaScript UDF that takes ' ORDER_JSON' as input and returns an array of product names. Use 'JSON.parse()' to parse the JSON string and iterate using array methods.
• C. Create a SQL UDF that iterates through the JSON array using SQL commands and returns a comma-separated string of product names. Then, use SPLIT TO_TABLE to convert the string to rows.
• D. Create a Python UDTF that takes 'ORDER JSON' as input, parses it, and yields a row for each product name extracted. Use LATERAL FLATTEN within the UDTF for optimized JSON processing.
• E. Use a standard SQL query with LATERAL FLATTEN and JSON VALUE functions to extract product names directly without using a UDF or UDTF.

You are developing a JavaScript stored procedure in Snowflake using Snowpark to perform a complex data transformation. This transformation involves multiple steps: filtering, joining with another table, and aggregating data'. You need to ensure that the stored procedure is resilient to failures and can be easily debugged. Which of the following practices would contribute to the robustness and debuggability of your stored procedure? (Select all that apply)

• A. Passing the 'snowflake' binding as an argument to each modular function to facilitate logging and SQL execution within those functions.
• B. Breaking down the complex transformation into smaller, modular functions within the stored procedure and testing each function independently.
• C. Using Snowpark's logging capabilities to record intermediate results and error messages at various stages of the transformation.
• D. Directly manipulating the Snowflake metadata (e.g., table schemas) within the stored procedure for dynamic schema evolution.
• E. Relying solely on try-catch blocks within the stored procedure to handle all potential exceptions.