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A data analyst is working with a table containing customer feedback text and needs to perform various text analysis tasks efficiently within Snowflake. They want to summarize the reviews, determine their sentiment, and extract specific pieces of information. Which of the following Snowflake Cortex LLM functions, when applied to a text column, will achieve the desired outcome and return the specified output type?

• A. To extract a specific answer to a question from each review, the analyst can use
  
• B. To determine the overall sentiment of each review, the analyst should use
  
• C. To get a concise overview of each review, the analyst should use
  
• D. To categorize reviews into predefined labels, the analyst should use
  
• E. The
  

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A Snowflake administrator is tasked with monitoring the efficiency and cost-effectiveness of their Cortex Analyst deployments. They need to identify if certain semantic models are generating a high volume of failed or expensive queries. Which of the following approaches or statements are crucial for effectively monitoring and identifying issues with Cortex Analyst usage and associated costs?

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

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database role. Which of the following 'privileges directly related to the virtual warehouse or task execution' are required for the process invoices_task' to successfully execute the Document AI '!PREDICT' method? (Select ALL that apply.)

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

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A company is building a chatbot for internal support, powered by Snowflake Cortex LLMs. The primary goals are to provide answers that are accurate, grounded in proprietary documentation, and to minimize factual 'hallucinations'. They are considering various strategies to achieve this. Which of the following statements correctly describe effective methods or tools within Snowflake for addressing these concerns?

• A. Deploying a custom fine-tuned model using SNOWFLAKE.CORTEX.FINETUNE on proprietary documentation is the most effective approach to ensure factual accuracy for any LLM task.
• B. Enabling Cortex Guard with guardrails: true directly addresses model hallucinations by ensuring responses are always factually correct and aligned with the provided context.
• C. Using Cortex Search as a Retrieval Augmented Generation (RAG) engine can enhance LLM responses by providing relevant context from proprietary documentation, thereby reducing hallucinations.
• D. AI Observability can be leveraged to systematically evaluate applications, measuring metrics like 'factual correctness and 'groundedness' to detect and mitigate hallucinations, especially in summarization.
• E. For tasks requiring LLMs to generate SQL queries from natural language, using the Cortex Analyst verified Query Repository (VQR) can improve accuracy by leveraging pre-verified SQL queries for similar questions.

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A financial institution wants to leverage Snowflake Cortex Agents to build an AI application for complex financial analysis, requiring interaction with both their structured transaction databases and unstructured legal documents, while also ensuring intelligent decision- making throughout the process. Which of the following accurately describe the foundational capabilities of Snowflake Cortex Agents?

• A. Allows integration of custom logic and external services through user-defined functions (UDFs) and stored procedures as custom tools.
• B. Cortex Agents enable direct fine-tuning of base LLMs using private customer data, with the resulting models managed within the Snowflake Model Registry.
• C. Agents are designed to orchestrate tasks by planning execution steps, and utilizing tools such as Cortex Analyst for structured data and Cortex Search for unstructured data.
• D. Cortex Agents primarily focus on providing a low-latency, hybrid (vector and keyword) search engine over text data.
• E. They incorporate a 'Reflection' component, allowing the agent to evaluate results after each tool use and determine the next logical steps, including iterating or clarifying.

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