A development team is implementing a document retrieval system in Snowflake. They plan to store document embeddings and use VECTOR_L2_DISTANCE to find the most relevant documents for a given query embedding. Considering Snowflake's capabilities, which of the following statements are true regarding the use of vector types and VECTOR_L2_DISTANCE
? (Select all that apply)

• A. To prevent issues with direct vector comparisons, explicitly using
• B. Using the Snowpark Python library, developers can directly invoke
  
• C. VECTOR
• D. O When defining a table column for 1024-dimensional float embeddings, the SQL type specification
• E. Document embeddings, which are typically float arrays, can be stored in a

A data engineering team is planning to build a real-time data pipeline using Snowflake's dynamic tables to process incoming log dat a. They want to use SNOWFLAKE. CORTEX. EXTRACT_ANSWER to pull out specific error codes and timestamps from log entries. They are also mindful of the operational costs. Which of the following statements accurately describes limitations or cost considerations for using SNOWFLAKE . CORTEX. EXTRACT_ANSWER in this scenario?

• A. If a source_document input exceeds the model's token limit, EXTRACT_ANSWER will automatically truncate the text, potentially impacting results but avoiding an error.
• B. EXTRACT_ANSWER is explicitly designed for multi-language extraction, making it suitable for logs that might contain various languages without affecting cost.
• C. To optimize performance and reduce cost for large-scale log processing, it is recommended to execute EXTRACT_ANSWER queries on a Snowpark-optimized warehouse of at least 'LARGE' size.
• D. The billing for EXTRACT_ANSWER is based on the combined token count of both the source_document (log entry) and the question used for extraction.
• E. The EXTRACT_ANSWER function does not support dynamic tables, which will prevent its direct use in this pipeline design.

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. Configuring the 'base_table' parameter to directly reference a dynamic table, ensuring real-time data ingestion and processing before SQL generation.
• 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. Specifying a dedicated 'CORTEX SEARCH SERVICE for every dimension to pre-compute all possible literal values, optimizing response time.
• E. Utilizing advanced data types like 'VARIANT' and 'OBJECT for all dimensions to accommodate semi-structured data without complex transformations.

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. Cortex Agents enable direct fine-tuning of base LLMs using private customer data, with the resulting models managed within the Snowflake Model Registry.
• B. Cortex Agents primarily focus on providing a low-latency, hybrid (vector and keyword) search engine over text data.
• C. Allows integration of custom logic and external services through user-defined functions (UDFs) and stored procedures as custom tools.
• D. 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.
• 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.

A Snowflake administrator needs to implement a granular access control strategy for LLMs. The general policy is to restrict access to a select few models via an account-level allowlist. However, a specific data science team (using role 'DATA SCIENCE TEAM ROLE) requires access to the 'claude-3-5-sonnet' model, which should not be available to other users or globally via the allowlist. Given this scenario, which set of commands would correctly establish this access control while adhering to the specified requirements?

• A.
  
• B.
  
• C.
  
• D.
  
• E.