GES-C01 Exam Dumps | A company is developing a RAG application to provide concise and highly relevant answers to user queries

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Question 133/171

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. 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.

B. 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.

C. 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'.

D. 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.

E. The

Correct Answer: B,C,E

Option A is incorrect. While 'snowflake-arctic-embed-l-v2.0-8k' has a larger context window, Snowflake explicitly recommends splitting text into chunks of no more than 512 tokens for optimal RAG retrieval quality. This practice typically results in higher retrieval and downstream LLM response quality, even for models with larger context windows. Option B is correct. The 'voyage-multilingual-2 model has a context window of 32000 tokens, while 'snowflake-arctic-embed-l-v2.0' has a 512-token context window. Both are multilingual embedding models. Option C is correct. For best search results with Cortex Search, Snowflake recommends splitting the text in your search column into chunks of no more than 512 tokens. Option D is incorrect. For 'EMBED TEXT functions, only 'input tokens' are counted towards the billable total, not output tokens. Option E is correct. The "SNOWFLAKE.CORTEX.COUNT_TOKENS function can be used to calculate the exact number of tokens in a string for a specified model, which is useful for managing context window limits and understanding cost implications.

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