A data engineer is developing an AI-infused data pipeline in Snowflake Notebooks to analyze Federal Reserve Meeting Minutes and official Statements, which are initially in PDF format. The goal is to determine the FED's stance on interest rates (hawkish, dovish, or neutral) and the reasoning for each ingested PDF using an LLM. The pipeline needs to automate data ingestion, text extraction, LLM inference, and store the results in a Snowflake table. Which sequence of operations and Snowflake features is most appropriate for building this pipeline within Snowflake?

• A. Load unstructured PDF files into an internal stage. Use a stored procedure to download new PDFs from the FOMC website. Leverage Snowpark Container Services to deploy a fine-tuned open-source LLM (e.g., Llama 2) for text extraction and sentiment analysis, and orchestrate the pipeline with ' Dynamic TableS for continuous updates.
• B. Scrape data from an external website directly into a Snowflake table using an 'EXTERNAL FUNCTION'. Then, apply 'SNOWFLAKE.CORTEX.EXTRACT ANSWER with a question like 'What is the FED's stance?' and 'SNOWFLAKE.CORTEX.SUMMARIZE' for reasoning to enrich the table. Automate this using 'STREAMS' and 'TASKS.
• C. Directly ingest PDF documents into a 'VARIANT column in a Snowflake table. Then, use the SQL function in 'OCR mode to extract text and layout. The extracted text is then passed to 'SNOWFLAKE.CORTEX.CLASSIFY TEXT to determine the sentiment, and the results are stored in a new table.
• D. Ingest PDF documents into a directory table. Use 'Document AI' C!PREDICT') to extract specific entities and tables from the PDFs into structured JSON. Then, create a 'STREAM' on the stage and a 'TASK' to continuously process new documents, extracting information and potentially performing additional sentiment analysis with another LLM.
• E. Scrape PDF data from an external website, load unstructured PDF files to an internal stage, then use a 'UDE to parse raw text from PDFs and a separate UDF' ('GENERATE_PROMPT) to encapsulate a custom prompt. Finally, use a 'TASK' to automate the process, calling Snowflake's function with the custom prompt at the point of ingestion to generate the sentiment signal and reasoning.

A data engineering team is setting up an automated pipeline to extract information from new invoices using Document AI. They've created a database and schema Cinvoice_db.invoice_schema') and a Document AI model build They then created an internal stage for documents. When they attempt to run the method on documents uploaded to 'invoice_stage' , they consistently receive the following error:

Given this error message, which 'corrective SQL command' addresses the most likely misconfiguration of the 'invoice_stage' to allow Document AI processing?

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

A company is implementing a Document AI solution to extract sensitive financial data from invoices. They plan to fine-tune the Document AI model (Arctic-TILT) and then manage this custom model within the Snowflake Model Registry. Which of the following statements correctly outlines the access control, data handling, and model management principles for this scenario?

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

A data engineer has successfully experimented with a prompt and various model settings in the Snowflake Cortex Playground for a text classification task using the mistral-large2 model and Cortex Guard. They now want to operationalize this solution within their Snowflake environment. Which of the following statements correctly describe capabilities or considerations when moving from the Cortex Playground to a production pipeline?

• A. To filter unsafe LLM responses in production, the Cortex Guard option, which is built with Meta's Llama Guard 3, must be explicitly enabled in the COMPLETE function's options argument.
• B. The exported SQL query, when used with dynamic tables, supports incremental refresh for efficient processing of new data without recomputing the entire table.
• C. If the mistral-large2 model is not natively available in the target production region, cross-region inference must be enabled by setting the CORTEX_ENABLED_CROSS REGION parameter.
• D. The Playground allows exporting the exact SQL query with all defined model settings, including temperature and Cortex Guard enablement, for direct use in a Snowflake worksheet or task.
• E. For continuous processing of new data, the exported SQL query can be automated using