A data scientist is leveraging various Snowflake Cortex LLM functions to process extensive text data for an application. To effectively manage their budget, they need a clear understanding of how costs are incurred for each specific function. Which of the following statements accurately describe how costs are calculated for Snowflake Cortex LLM functions, with a particular focus on token usage?

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

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A business analyst is using a Cortex Analyst-powered conversational application to query structured data in Snowflake. They initially ask, 'What was the total profit from California last quarter?' and then follow up with, 'What about New York?' The application successfully provides accurate answers to both questions. Which of the following statements explain how Cortex Analyst supports this multi-turn conversational experience and maintains accuracy? (Select all that apply)

• A.Cortex Analyst stores the full, verbatim history of all previous user prompts and LLM responses, which are then passed to every subsequent LLM call to ensure complete context retention without any summarization.
• B.To handle follow-up questions, Cortex Analyst leverages an internal LLM summarization agent (e.g., Llama 3.1 70B) to reframe the current-turn question by retrieving context from the conversation history, rather than simply passing the entire history.
• C.The accuracy of the SQL queries generated by Cortex Analyst for follow-up questions is significantly enhanced by its integration with a Verified Query Repository (VQR), which stores pre-verified natural language questions and their corresponding SQL queries.
• D.For multi-turn conversations, Cortex Analyst primarily relies on semantic search over sample values defined in the semantic model to infer context and generate SQL, making explicit conversation history management unnecessary.
• E.The semantic model YAML file, which defines logical tables, dimensions, and measures, is crucial for Cortex Analyst to bridge the gap between business terminology and underlying technical schema, thereby improving text-to-SQL conversion accuracy for both initial and follow-up queries.

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A data analytics team is building a self-service analytics application using Snowflake Cortex Analyst to allow business users to query sales data with natural language. They are defining a semantic model in YAML to ensure accurate text-to-SQL generation. Which of the following is the most crucial aspect of the semantic model's configuration for Cortex Analyst to effectively translate natural language into SQL for structured data?

• A.Specifying a dedicated 'CORTEX SEARCH SERVICE for every dimension to pre-compute all possible literal values, optimizing response time.
• B.Defining a comprehensive 'verified_queries' section with a high volume of example natural language questions and their exact SQL translations to handle all potential user queries.
• C.Providing detailed 'name', 'description' , and 'synonyms' for logical tables, dimensions, and facts to bridge the gap between business terminology and the underlying database schema.
• D.Configuring the 'base_table' parameter to directly reference a dynamic table, ensuring real-time data ingestion and processing before SQL generation.
• E.Utilizing advanced data types like 'VARIANT' and 'OBJECT for all dimensions to accommodate semi-structured data without complex transformations.

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A data scientist is tasked with improving the accuracy of an LLM-powered chatbot that answers user questions based on internal company documents stored in Snowflake. They decide to implement a Retrieval Augmented Generation (RAG) architecture using Snowflake Cortex Search. Which of the following statements correctly describe the features and considerations when leveraging Snowflake Cortex Search for this RAG application?

• A.Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
• B.To create a Cortex Search Service, one must explicitly specify an embedding model and manually manage its underlying infrastructure, similar to deploying a custom model via Snowpark Container Services.
• C.For optimal search results with Cortex Search, source text should be pre-split into chunks of no more than 512 tokens, even when using models with larger context windows like
  
• D.The
  
• E.Enabling change tracking on the source table for the Cortex Search Service is optional; the service will still refresh automatically even if change tracking is disabled.

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A data science team is planning to implement a new RAG (Retrieval Augmented Generation) application using Snowflake Cortex, specifically leveraging Cortex Search. They are evaluating the key features, best practices, and cost considerations associated with Cortex Search. Which of the following statements accurately describe aspects of Cortex Search?

• A.Cortex Search automatically handles embedding, infrastructure maintenance, and ongoing index refreshes, and can be used as a backend for enterprise search or a RAG engine for LLM chatbots.
• B.The credit cost for Cortex Search Services is primarily based on the number of queries executed against the service, not the amount of indexed data.
• C.Cortex Search supports only English-only embedding models; multilingual RAG applications require external embedding solutions.
• D.For best search results, Snowflake recommends splitting text in the search column into chunks of no more than 512 tokens, even when longer-context embedding models are available.
• E.Cortex Search Services require a virtual warehouse for initial setup and subsequent refreshes to run queries against base objects and build the search index.

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