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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) 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.
B) The underlying model for Cortex Guard is Meta's Llama Guard 3, and its processing costs are separate from the primary LLM inference costs.
C) 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.
D) 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.
E) 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.

2. An operations team at a company is implementing a robust governance framework to monitor and optimize the costs associated with their Snowflake Cortex LLM function usage. They need to identify which functions are driving the highest token consumption and overall credit usage to pinpoint areas for cost reduction. Which of the following monitoring tools or methods are appropriate for gaining these insights into Cortex LLM function costs and token consumption?

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

3. 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) Agents are designed to orchestrate tasks by planning execution steps, and utilizing tools such as Cortex Analyst for structured data and Cortex Search for unstructured data.
B) Cortex Agents primarily focus on providing a low-latency, hybrid (vector and keyword) search engine over text data.
C) Cortex Agents enable direct fine-tuning of base LLMs using private customer data, with the resulting models managed within the Snowflake Model Registry.
D) Allows integration of custom logic and external services through user-defined functions (UDFs) and stored procedures as custom tools.
E) They incorporate a 'Reflection' component, allowing the agent to evaluate results after each tool use and determine the next logical steps, including iterating or clarifying.

4. A data engineering team wants to deploy a proprietary PyCaret classification model, saved as pycaret_best_model.pkl, for inference within Snowpark Container Services (SPCS). They need to register this custom model in the Snowflake Model Registry. Which of the following is a correct and essential step in this process?

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

5. An organization is planning to deploy Snowflake Cortex Agents for sensitive financial reporting, requiring strict adherence to data governance policies and clear understanding of cost drivers. Which of the following statements about governance and cost considerations for Cortex Agents are true?

A) To use a semantic model with Cortex Agents, the role executing the agent request requires only the SNOWFLAKE .CORTEX_AGENT_USER role, as it implicitly inherits all necessary privileges for semantic model access.
B) Usage costs for Cortex Agents are primarily driven by the number of tokens processed by the underlying LLMs for orchestration and the compute/service costs of tools like Cortex Analyst and Cortex Search.
C) Monitoring of Cortex Agent interactions for debugging and refinement is exclusively performed through internal Snowflake system logs, with no external SDK support.
D) Cortex Agents are powered by Snowflake-hosted LLMs by default, ensuring that all customer data and prompts remain within Snowflake's governance boundary.
E) The CORTEX_DOCUMENT_PROCESSING_USAGE_HISTORY view provides detailed credit consumption for Cortex Agent activities, including orchestration steps and tool usage.

Solutions: