Snowflake GES-C01 echte frage : SnowPro® Specialty: Gen AI Certification Exam

A data engineering team is building a Retrieval Augmented Generation (RAG) pipeline that heavily relies on 'SNOWFLAKE.CORTEX.EMBED_TEXT 768' to process millions of documents daily. They need to optimize for both cost and retrieval quality. Which of the following statements are true regarding the cost and performance of 'EMBED_TEXT 768' in Snowflake? (Select all that apply)

• A. For optimal retrieval quality in RAG scenarios, text should be split into chunks of no more than 512 tokens before being passed to 'EMBED TEXT 768', even if the model supports a larger context window.
• B. The 'snowflake-arctic-embed-m-vl .5 model, used by 'EMBED TEXT 768', has a context window of 512 tokens, and texts exceeding this length are truncated before embedding.
• C. To minimize costs for ' EMBED_TEXT 768 operations, it is recommended to execute queries using a smaller virtual warehouse (no larger than MEDIUM), as larger warehouses do not improve performance for these functions.
• D. The 'EMBED_TEXT 768' function is billed based on the number of 'output tokens' generated by the embedding model, as this represents the computational complexity of the vector.
• E. The 'EMBED TEXT 768' function, regardless of the 768-dimension model used, has a fixed cost of 1.50 Credits per one million Tokens processed.

A company is developing a RAG application to provide concise and highly relevant answers to user queries from a vast knowledge base of technical documents. They are using Cortex Search for retrieval and are considering different embedding models and text chunking strategies to optimise the system. Which of the following statements about Cortex Search embedding models and RAG best practices are correct? (Select all that apply)

• A. The 'voyage-multilingual-2 model is suitable for multilingual documents and has a significantly larger context window (32000 tokens) compared to 'snowflake- arctic-embed-l-v2.C (512 tokens), making it more robust for longer text inputs.
• B. Using the 'snowflake-arctic-embed-l-v2.0-8k' model, which has an 8192-token context window, allows processing entire large technical documents as a single chunk for embedding, leading to better RAG results by preserving full document context.
• C. The cost for embedding models in Cortex Search, such as 'snowflake-arctic-embed-l-v2.0' and 'e5-base-v2, is incurred based on both input and output tokens.
• D. For optimal RAG retrieval quality with Cortex Search, it is recommended to split text into chunks of no more than 512 tokens, even when using models with larger context windows like 'snowflake-arctic-embed-l-v2.0-8k'.
• E. The
  

A legal department uses Snowflake to manage and review large volumes of contracts. They need to automate the process of finding specific pieces of information, such as the effective_date or involved_parties, from these unstructured contract texts. They are considering using SNOWFLAKE. CORTEX. EXTRACT_ANSWER. Which characteristic correctly describes the primary intent or behavior of SNOWFLAKE. CORTEX. EXTRACT_ANSWER, distinguishing it from other LLM functions?

• A. It focuses on identifying and returning a direct answer to a specific question contained within the provided source document.
• B. It classifies an input text into one of several predefined categories provided by the user.
• C. It is primarily designed to generate entirely new text based on a given prompt, much like a conversational AI.
• D. It transforms an input document into a summarized version, reducing its length while preserving key information.
• E. It evaluates a text and returns a numeric score indicating the overall positive or negative sentiment.

A security architect is configuring access controls for a new custom role, 'document_processor_role' , which will manage Document AI operations within a designated database 'doc_processing_db' and schema 'doc_workflow_schema'. The goal is to grant only the minimum essential database-level role required to begin working with Document AI features.

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

A business user frequently asks Cortex Analyst questions that require filtering on specific product names, such as "What were the sales for 'iced tea' last month?" The 'product' dimension has many distinct values (high cardinality), and Cortex Analyst sometimes struggles to accurately identify the exact literal product name, leading to less precise SQL queries. The Gen AI Specialist wants to enhance Cortex Analyst's ability to find these literal values for the 'product' dimension. To improve Cortex Analyst's literal search capability for the high-cardinality 'product' dimension, which of the following is the most appropriate and recommended approach to configure in the semantic model?

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