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Snowflake SnowPro® Specialty: Gen AI Certification 認定 GES-C01 試験問題:

1. An AI developer is building a Snowflake data pipeline to prepare unstructured data for a RAG application. The pipeline involves extracting text, splitting it into chunks, generating embeddings, and then indexing for Cortex Search. Considering the role of helper functions like SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER
, which of the following statements accurately describes its typical operational placement and interaction within this Gen AI pipeline?

A) Its output, consisting of smaller text chunks, serves as the direct input for text embedding functions that then convert these chunks into vector representations for semantic indexing.
B) It replaces the need for
C) It is typically applied after an embedding function (e.g.,
D) It is a post-processing step for LLM-generated responses, used to break down long answers into digestible paragraphs for user display in chat interfaces.
E) The function's recursive nature enables it to automatically detect and correct factual inconsistencies or 'hallucinations' present in the original large text documents before they are embedded.

2. A Gen AI engineer is tasked with selecting the most suitable Large Language Model (LLM) from Snowflake Cortex AI for a new customer service chatbot. They need to rapidly prototype and compare different LLMs with varying parameters on a sample dataset before committing to a production deployment. Which of the following statements accurately describe how the Cortex Playground (Public Preview) can assist in this scenario?

A) It enables side-by-side comparison of model outputs for different LLMs and model settings, facilitating an informed decision on model selection.
B) It provides a mechanism to deploy the chosen LLM directly into Snowpark Container Services (SPCS) compute pools from within the playground for immediate production use.
C) It supports exporting the tested prompts and model configurations as Python code, ready for integration into a Snowpark ML pipeline.
D) It allows direct fine-tuning of selected LLMs with custom datasets within the playground interface to improve model performance for specific tasks.
E) It allows connection to a Snowflake table with textual data, processing up to 100 rows, to experiment with prompts directly on actual data.

3. A machine learning team has fine-tuned a llama3.1-70b model for a specialised task using Snowflake Cortex Fine-tuning, named prod_llama_responder. They now need to deploy this model for inference via the Cortex REST API across different Snowflake regions and manage its lifecycle effectively. Which of the following statements regarding the fine-tuned model's deployment, access, and management are accurate?

A) Option B
B) Option E
C) Option A
D) Option C
E) Option D

4. A financial data team is implementing a Snowflake Cortex AI solution to summarize regulatory documents using SNOWFLAKE.CORTEX.TRY_COMPLETE They aim for both cost efficiency and high reliability, especially when dealing with documents that might occasionally exceed model context limits or result in malformed output. Which of the following statements about the cost and operational behavior of TRY_COMPLETE are TRUE in this context? (Select all that apply)

A) Option B
B) Option E
C) Option A
D) Option C
E) Option D

5. 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 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.
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

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 '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.