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An ML engineer is designing a Cortex Agent to provide highly accurate and contextualized responses. They intend for the agent to use state-of-the-art LLMs for orchestration and to maintain a specific brand tone in its outputs. Considering the available models and configurations for Cortex Agents, which statement is true?

• A. Cortex Agents primarily interact with data through the Snowflake Model Registry API to retrieve and update fine-tuned model parameters during their iterative planning phase.
• B. To ensure the agent's responses adhere to a desired brand and tone, 'Response instructions' can be configured, which guide the agent's output style and persona.
• C. When an agent utilizes an LLM for orchestration, the system ensures that cross-region inference is automatically enabled without any latency implications, making region selection irrelevant.
• D. Cortex Agents are restricted to using only Snowflake Arctic models for orchestration, due to security and governance requirements, and must operate in the account's default region.
• E. The agent's 'Planning' component is specifically responsible for evaluating results after each tool use and deciding the subsequent steps in the query resolution process, acting as a feedback loop.

A Gen AI Specialist is developing a conversational analytics application using Cortex Analyst, aiming to provide a seamless multi-turn conversation experience for business users querying structured dat a. The team observes that follow-up questions are sometimes misinterpreted, especially when the conversation history is long. Which of the following statements accurately describe how Cortex Analyst handles multi-turn conversations and key considerations for optimizing this functionality?

• A. To address misinterpretation in long conversations, the max_tokens parameter for the Cortex Analyst REST API should be significantly increased to ensure the LLM receives the complete historical context without truncation.
• B. Multi-turn conversation in Cortex Analyst is primarily handled by the CORTEX_ANALYST_MODEL AZURE_OPENAI parameter, which, when enabled, allows Azure OpenAl models to manage conversational context more effectively.
• C. Cortex Analyst simply passes the entire conversation history to all subsequent LLM calls, and optimizing this requires manually truncating the array in messages the REST API request.
• D. Cortex Analyst incorporates an additional LLM summarization agent before its original workflow to rewrite current-turn questions based on conversation history, with Llama 3.1 70B being a recommended model for this task due to its performance in evaluating summarization quality.
• E. When a user shifts intent frequently in a multi-turn conversation, Cortex Analyst automatically resets the conversation history to prevent misinterpretations and improve accuracy.

A data engineering team is implementing a Document AI solution to automate the extraction of vendor invoice details. They have already published a Document AI model build, 'vendor_invoice_model', located in New invoices are uploaded to an internal stage, 'fin db.invoice_processing_schema.raw invoices_stage'. The 'invoice_pipeline role' is responsible for executing the
! PREDICT
method on these staged documents as part of an automated task. Which of the following USAGE privileges are essential for to successfully execute the
! PREDICT
method and process documents from the specified stage, assuming the required SNOWFLAKE.DOCUMENT_INTELLIGENCE_CREATOR role is already granted?

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

A data scientist needs to fine-tune a 'mistral-7b' LLM using Snowflake Cortex for a specific text summarization task. They have prepared a training dataset in a Snowflake table, with text in a column named 'source_text' and the desired summaries in a column named 'expected_summary' . They also want to understand the cost implications. Which SQL statement will correctly initiate the fine-tuning job, and how will the cost be primarily calculated?

• A. The fine-tuning job is initiated by:
  
• B. The fine-tuning job is initiated by:
  
• C. The fine-tuning job is initiated by:
  
• D. The fine-tuning job is initiated by:
  
• E. The fine-tuning job is initiated by providing the prompt and completion data directly as arrays within the 'FINETUNE' function call, avoiding the need for a separate training data table, and costs are only for the storage of the fine-tuned model.

An enterprise is deploying a Cortex Analyst application and needs to manage its cost, ensure data security, and understand its operational behavior within Snowflake. Which of the following statements are true regarding the deployment, cost, and security of Cortex Analyst and its semantic models?

• A. Enabling the account parameter is the recommended approach for using Azure OpenAI models with Cortex Analyst to ensure the highest performance and adherence to RBAC restrictions.
• B. Semantic models for Cortex Analyst, stored as YAML files in a Snowflake stage, should have their stage access controlled by RBAC to implicitly control access to the underlying tables referenced in the semantic model.
• C. Cortex Analyst applications are fully managed, and by default, all data, including metadata and prompts, remain within Snowflake's governance boundary when using Snowflake-hosted LLMs from Mistral and Meta.
• D. The CORTEX_ANALYST_USER database role is sufficient for making requests to Cortex Analyst, and the cost incurred is solely based on the number of tokens processed by the underlying LLMs, not per message.
• E. Administrators can monitor Cortex Analyst requests, including the user, question asked, generated SQL, and errors, by querying the SNOWFLAK LOCAL. CORTEX ANALYST_REQUESTS function.