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

An ML engineer is preparing a Docker image for a custom LLM application that will be deployed to Snowpark Container Services (SPCS). The application uses a mix of packages, some commonly found in the Snowflake Anaconda channel and others from general open-source repositories like PyPI. They have the following Docker-file snippet and need to ensure the dependencies are correctly installed for the SPCS environment to support a GPU workload. Which of the following approaches for installing Python packages in the Dockerfile would ensure a robust and compatible setup for a custom LLM running in Snowpark Container Services, based on best practices for managing dependencies in this environment?

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

A Snowflake user attempts to access the Cortex Playground to experiment with LLM functions but receives an error indicating insufficient privileges. They have an active Snowflake session. Which of the following steps must be taken to ensure they can successfully use the Cortex Playground?

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

A team is developing a critical business intelligence application that leverages Snowflake Cortex Analyst to provide natural language querying capabilities over complex structured dat a. To minimize operational costs while maintaining high accuracy, which of the following strategies are most effective for optimizing the cost efficiency of the Cortex Analyst service?

• A. Configuring a custom instruction with a short, precise task description to reduce the input token count for the LLMs orchestrating SQL generation.
• B. Leveraging Cortex Search Services integration within the semantic model to improve literal value matching, thereby reducing the need for Cortex Analyst to perform expensive fuzzy string matching or re-prompt the user.
• C. Using a smaller, less capable LLM as the underlying summarization agent for multi-turn conversations to reduce token processing costs, even if it slightly degrades conversational context.
• D. Implementing a comprehensive Verified Query Repository (VQR) to guide Cortex Analyst towards pre-validated SQL queries for common questions, which ensures predictable execution and reduces LLM inference iterations.
• E. Optimizing the semantic model YAML file by reducing the number of logical tables and columns to decrease the metadata processed by Cortex Analyst's LLMs per message.

A data application developer is tasked with building a multi-turn conversational AI application using Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To ensure the conversation flows naturally and the LLM maintains context from previous interactions, which of the following is the most appropriate method for handling and passing the conversation history?

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

A Gen AI Specialist is developing a conversational analytics application using Cortex Analyst, aiming to provide a seamless multi-turn conversation experience for business users querying structured dat a. The team observes that follow-up questions are sometimes misinterpreted, especially when the conversation history is long. Which of the following statements accurately describe how Cortex Analyst handles multi-turn conversations and key considerations for optimizing this functionality?

• A. Multi-turn conversation in Cortex Analyst is primarily handled by the CORTEX_ANALYST_MODEL AZURE_OPENAI parameter, which, when enabled, allows Azure OpenAl models to manage conversational context more effectively.
• B. Cortex Analyst incorporates an additional LLM summarization agent before its original workflow to rewrite current-turn questions based on conversation history, with Llama 3.1 70B being a recommended model for this task due to its performance in evaluating summarization quality.
• C. To address misinterpretation in long conversations, the max_tokens parameter for the Cortex Analyst REST API should be significantly increased to ensure the LLM receives the complete historical context without truncation.
• D. When a user shifts intent frequently in a multi-turn conversation, Cortex Analyst automatically resets the conversation history to prevent misinterpretations and improve accuracy.
• E. Cortex Analyst simply passes the entire conversation history to all subsequent LLM calls, and optimizing this requires manually truncating the array in messages the REST API request.