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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineering team is preparing a large corpus of unstructured text documents for a Retrieval Augmented Generation (RAG) application in Snowflake, leveraging Cortex Search and LLM functions. They plan to use SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER as part of their data ingestion pipeline. What is the primary benefit of employing this helper function in the context of their RAG workflow?

A) It automatically translates documents into a target language, ensuring multilingual compatibility for the LLM.
B) It compresses the text data to reduce storage costs in Snowflake stages before processing by embedding models.
C) It performs sentiment analysis on each chunk, allowing the RAG system to filter out negative or irrelevant content before retrieval.
D) It divides lengthy documents into smaller, manageable text chunks, which improves the precision of information retrieval and the relevance of downstream LLM responses.
E) It generates vector embeddings for each document chunk, eliminating the need for separate embedding models.

2. 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) CHANGE_TRACKING
B) For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.
C) The

D) The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GBImonth) having a minimal impact on overall billing.
E) For embedding text, selecting a model like

3. An administrator has configured the 'CORTEX_MODELS_ALLOWLIST parameter to only permit the 'mistral-large? model at the account level. A user with the 'PUBLIC' role, which has been granted 'SNOWFLAKE.CORTEX USER and 'SNOWFLAKE."CORTEX- MODEL-ROLE-LLAMA3.1-70B"' , attempts to execute several 'AI COMPLETE queries. Which of the following queries will successfully execute?

A)

B)

C)

D)

E)

4. An enterprise is designing an advanced generative AI application in Snowflake, leveraging Cortex Agents to orchestrate data analysis from both structured and unstructured sources. According to Snowflake's Gen AI principles and the capabilities of Cortex Agents, which of the following statements accurately describe the workflow components and the types of tools an agent can utilize?

A) For debugging, Cortex Agents allow direct modification of the LLM's internal state to refine accuracy, latency, and cost during execution.
B) Cortex Agents primarily focus on pre-defined, single-turn SQL queries for structured data, with limited support for unstructured data processing.
C) Cortex Agents are restricted to using only Snowflake's native Cortex LLM functions; custom logic via UDFs or stored procedures is not supported for tool implementation.
D) The agent's workflow includes 'Planning' to orchestrate a solution, 'Explore options' for disambiguation, and 'Reflection' to determine next steps after tool use. Supported tools include Cortex Analyst and Cortex Search.
E) Cortex Agents can orchestrate across both structured and unstructured data sources, and custom tools can be implemented using Snowflake stored procedures and user-defined functions (UDFs).

5. A development team is constructing a Gen AI application using Snowflake Cortex LLM functions, particularly for conversational and text generation tasks. They are concerned about potential high costs due to token consumption. Which of the following strategies would most effectively help minimize token usage and optimize costs when working with these Cortex LLM functions?

A) For multi-turn conversational experiences using SNOWFLAKE .CORTEX.COMPLETE, only send the most recent user prompt in each API call, as the model automatically retains previous context.
B) When employing AI_COMPLETE for structured output tasks, providing concise and highly descriptive explanations for each field within the JSON schema will reduce the input tokens required for the LLM to understand and adhere to the schema accurately.
C) Utilize the COUNT_TOKENS (SNOWFLAKE .CORTEX) helper function to pre-validate the prompt length against the model's context window, thereby preventing truncation errors and subsequent re-runs.
D) In multi-turn conversations within Cortex Analyst, integrate a dedicated LLM summarization agent to rephrase follow-up questions, which reduces the total conversational history passed as context to the main LLM.
E) To encourage more succinct LLM responses and reduce completion_tokens, configure the temperature option to a higher value (e.g., 0.7) in COMPLETE function calls.

Solutions: