A data scientist fine-tuned a mistral-lb model in Snowflake for a specific customer support response generation task, naming it my_custom_responder_model. They now want to make this model available for AI_COMPLETE calls in production, ensuring proper access control and regional availability. Which of the following statements is true regarding the deployment and management of this fine-tuned model in Snowflake?

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

A data analyst needs to use SNOWFLAKE. CORTEX. EXTRACT_ANSWER to streamline information retrieval from various contract documents. They are new to Cortex functions and want to understand access requirements and optimal usage. Which of the following statements about using SNOWFLAKE .CORTEX. EXTRACT_ANSWER are correct?

• A. If EXTRACT_ANSWER encounters an unresolvable issue during processing, it returns NULL instead of an error, similar to TRY_COMPLETE.
• B. The analyst's role must be granted the SNOWFLAKE. CORTEX_USER database role to execute EXTRACT_ANSWER functions.
• C. EXTRACT_ANSWER is the most current and recommended function for all text extraction tasks, offering multi-label and image extraction capabilities.
• D. For optimal accuracy, the source_document input should be in plain English, and the question should be specific, asking for a single value.
• E. It is generally recommended to process multiple documents by applying EXTRACT_ANSWER to a table column containing the document texts, allowing for efficient batch processing.

A company is developing a Streamlit application leveraging Snowflake Cortex Analyst for natural language querying over sales data.
They want to implement a robust multi-turn conversational experience where users can ask follow-up questions. Which of the following statements accurately describe the design and cost implications of supporting multi-turn conversations in Cortex Analyst? (Select all that apply)

• A. The cost for Cortex Analyst's multi-turn conversational support is primarily based on the number of messages processed, and the number of tokens within each message does not directly affect the per-message cost.
• B. Developers can manually implement multi-turn conversations in their applications by using the
• C. Cortex Analyst supports multi-turn conversations by simply passing the entire conversation history directly to every LLM call within its agentic workflow, which is the most efficient method for maintaining context.
• D. When an LLM judge is used to evaluate the summarization quality for multi-turn conversations, a smaller model like Llama 3.1 8B is generally preferred over Llama 3.1 70B to minimise latency, even if it leads to a slightly higher error rate in rewritten questions.
• E. An internal LLM summarization agent is automatically employed by Cortex Analyst before its original workflow to reframe follow-up questions based on conversation history, optimising LLM processing for each agent.

A data operations team is attempting to scale up their Document AI automated pipeline. They are using a Snowflake Task to process a large volume of daily scanned invoices and receipts, which are stored in an internal stage 'financial_docs stage'. The current processing involves documents that are frequently around 75 MB each, and often there are batches exceeding 1 ,200 documents in a single day. The pipeline is failing consistently. Which of the following factors could be contributing to the failures in this Document AI automated pipeline? (Select all that apply.)

• A. The was created without specifying 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.
• B. The individual document size (75 MB) exceeds the maximum supported file size for Document AI.
• C. The number of documents in a single daily batch (exceeding 1 ,200) surpasses Document AI's processing limit per query.
• D. The account role executing the task lacks the 'SNOWFLAKCORTEX USER database role, which is a prerequisite for Document AI functions.
• E. The Snowflake Task is configured as a 'SERVERLESS TASK, which is not supported by Document AI.