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

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. The
  
• D. 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.
• E. 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
  

A compliance officer is reviewing the usage of Snowflake Cortex LLM functions and the Cortex REST API within their organization, specifically focusing on the implementation and impact of Cortex Guard. They observe several instances where 'guardrails' were enabled. Which of the following statements accurately describe the behavior and cost considerations of Cortex Guard when integrated with Snowflake Cortex LLM functions or the Cortex REST API?

• A. When Cortex Guard is enabled and a response is deemed unsafe, the LLM-generated output is replaced with a predefined message, and only the input tokens for Cortex Guard processing ('guard_tokens') are billed, not the potentially unsafe completion tokens.
• B. Cortex Guard operates by evaluating responses after the LLM has fully generated its content, and it incurs additional compute cost for both input and output tokens during its processing.
• C. Cortex Guard is inherently part of all Cortex LLM functions and does not require explicit enablement via 'guardrails: TRUE for SQL functions or the REST API.
• D. The underlying model for Cortex Guard is Meta's Llama Guard 3, and its processing costs are separate from the primary LLM inference costs.
• E. Cortex Guard can be configured with a custom message using the argument in the options object for both 'COMPLETE SQL function and the Cortex REST API.

A team of data application developers is leveraging Snowflake Copilot to streamline the creation of analytical SQL queries within their Streamlit in Snowflake application. They observe that Copilot sometimes struggles with complex joins or provides suboptimal queries when dealing with a newly integrated, deeply nested dataset. Based on Snowflake's best practices and known limitations, which actions or considerations would help improve Copilot's performance in this scenario?

• A. Break down complex requests into simpler, multi-turn questions, as Copilot is designed to build complex queries through conversational refinement and follow-up questions.
• B. Enable the CORTEX_MODELS_ALLOWLIST parameter to restrict Copilot to only use the largest available LLMs, thereby guaranteeing higher accuracy for complex queries.
• C. Implement curated views with descriptive and easy-to-understand names for views and columns, appropriate data types, and pre-define common/complex joins to simplify the underlying schema for Copilot.
• D. Ensure that a database and schema are explicitly selected for the current session, and that column names are meaningful, to provide Copilot with better context for query generation.
• E. Grant Copilot direct access to the raw data using ACCOUNTADMIN privileges, allowing it to infer schema relationships more effectively from data content.

A data team has implemented a Snowflake data pipeline using SQL tasks that process customer call transcripts daily. This pipeline relies heavily on SNOWFLAKE. CORTEX. COMPLETE() (or its updated alias) for various text analysis tasks, such as sentiment analysis and summary generation. Over time, they observe that the pipeline occasionally fails due to LLM-related errors, and the compute costs are higher than anticipated. What actions should the team take to improve the robustness and cost-efficiency of this data pipeline? (Select all that apply.)

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

A developer is building a client application that interacts with a Snowflake Cortex Agent using its REST API. They are implementing multi- turn conversation support. Which of the following is the most critical aspect for maintaining conversational context over multiple API calls?

• A. The agent internally summarizes long conversations, and the client receives only a condensed summary_token to pass for subsequent turns.
• B. A specific session_id parameter, generated at the start of the conversation, must be sent with each request to link turns.
• C. The client application must include all prior user prompts and assistant/analyst responses in a messages array in each new API request.
• D. The REST API automatically identifies and reuses context from previous requests based on the Authorization token, eliminating the need to pass full history.
• E. Only the last user prompt and the immediately preceding agent response should be sent to conserve token usage.