Free Snowflake GES-C01 Practice Test & Real Exam Questions

A data governance team is concerned about the consistency and compliance of SQL queries generated by Cortex Analyst for sensitive financial reporting. They need to ensure that all generated SQL for a specific semantic model always includes a 'WHERE' clause that filters data for 'region = 'EMEA" and adheres to 'ISO 8601' date formatting for all date columns, regardless of the user's natural language input. Which of the following approaches is the MOST effective for implementing these strict, overarching requirements within Cortex Analyst's semantic model?

An enterprise is deploying a new RAG application using Snowflake Cortex Search on a large dataset of customer support tickets. The operations team is concerned about managing compute costs and ensuring efficient index refreshes for the Cortex Search Service, which needs to be updated hourly. Which of the following considerations and configurations are relevant for optimizing cost and performance of the Cortex Search Service in this scenario?

A Snowflake developer, 'AI _ ENGINEER , is creating a Streamlit in Snowflake (SiS) application that will utilize a range of Snowflake Cortex LLM functions, including SNOWFLAKE. CORTEX. COMPLETE, SNOWFLAKE .CORTEX.CLASSIFY TEXT, and SNOWFLAKE. CORTEX. EMBED TEXT 768. The application also needs to access data from tables within a specific database and schem a. 'AI _ ENGINEER has created a custom role, for the application to operate under. Which of the following privileges or roles are absolutely necessary to grant to for the successful execution of these Cortex LLM functions and interaction with the specified database objects? (Select all that apply.)

A company is implementing a Document AI solution to extract sensitive financial data from invoices. They plan to fine-tune the Document AI model (Arctic-TILT) and then manage this custom model within the Snowflake Model Registry. Which of the following statements correctly outlines the access control, data handling, and model management principles for this scenario?

A data engineering team is setting up an automated pipeline in Snowflake to process call center transcripts. These transcripts, once loaded into a raw table, need to be enriched by extracting specific entities like the customer's name, the primary issue reported, and the proposed resolution. The extracted data must be stored in a structured JSON format in a processed table. The pipeline leverages a SQL task that processes new records from a stream. Which of the following SQL snippets and approaches, utilizing Snowflake Cortex LLM functions, would most effectively extract this information and guarantee a structured JSON output for each transcript?

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 Streamlit application developer wants to use AI_COMPLETE (the latest version of COMPLETE (SNOWFLAKE. CORTEX)) to process customer feedback. The goal is to extract structured information, such as the customer's sentiment, product mentioned, and any specific issues, into a predictable JSON format for immediate database ingestion. Which configuration of the AI_COMPLETE function call is essential for achieving this structured output requirement?

A financial institution wants to leverage Snowflake Cortex Agents to build an AI application for complex financial analysis, requiring interaction with both their structured transaction databases and unstructured legal documents, while also ensuring intelligent decision- making throughout the process. Which of the following accurately describe the foundational capabilities of Snowflake Cortex Agents?

A data engineering team is designing a scalable data pipeline in Snowflake that involves processing large text inputs with Cortex AI LLM functions. They want to ensure cost efficiency and prevent queries from failing due to exceeding LLM context window limits. They plan to use SNOWFLAKE. CORTEX. COUNT_TOKENS for pre-validation. Which of the following statements are TRUE about the role and cost of COUNT_TOKENS in this scenario? (Select all that apply)

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?