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Snowflake GES-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Snowflake Gen AI Governance	22%	- Cost management and token-based pricing - AI governance framework and policies - Audit and compliance for AI workloads - Guardrails, safety controls, and bias mitigation - Monitoring, logging, and observability
Topic 2: Snowflake Gen AI & LLM Functions	40%	- Cortex LLM functions: COMPLETE, CLASSIFY_TEXT, EXTRACT_ANSWER, SENTIMENT, SUMMARIZE, TRANSLATE - RAG implementation in Snowflake - Embedding functions: EMBED_TEXT_*, vector storage and similarity search - API integration and authentication - Model deployment with Snowpark Container Services and Model Registry
Topic 3: Snowflake for Gen AI Overview	26%	- Role-based access control (RBAC) for AI resources - Cortex AI components: Cortex Search, Cortex Analyst, Cortex LLMs - Snowflake Copilot and AI assistant capabilities - Snowflake Gen AI principles and best practices
Topic 4: Snowflake Document AI	12%	- Data extraction and structured output - Document AI setup and configuration - Performance optimization and troubleshooting - Document preparation and processing

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data processing team is using Snowflake Document AI to extract data from incoming supplier invoices. They observe that many documents are failing to process, and successful extractions are taking longer than expected, leading to increased costs. Upon investigation, they find error messages such as

. Additionally, their 'X-LARGE virtual warehouse is constantly active, contributing to higher-than-anticipated bills. Which two of the following actions are essential steps to troubleshoot and address the root causes of these processing errors and optimize their Document AI pipeline?

A) Scale down the virtual warehouse to 'X-SMALL' or 'SMALL' size, as larger warehouses do not increase Document AI query processing speed and incur unnecessary costs.
B) Increase the 'max_tokens' parameter within the '!PREDICT' function options to accommodate longer document responses from the model.
C) Implement a pre-processing step to split documents exceeding 125 pages or 50 MB into smaller, compliant files before loading to the stage.
D) Redefine extraction questions to be more generic and encompassing, reducing the number of distinct questions needed per document.
E) Configure the internal stage used for storing invoices with 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.

2. A development team is implementing a suite of generative AI applications on Snowflake, utilizing both SQL functions and the Cortex REST API. They prioritize content safety and plan to integrate Cortex Guard wherever possible. Considering the various interfaces for interacting with Snowflake Cortex LLMs, which of the following interfaces and functions support the direct use of Cortex Guard via the guardrails' argument or equivalent configuration?

A) The 'SNOWFLAKE.CORTEX.CLASSIFY_TEXT SQL function for text classification tasks.
B) The 'SNOWFLAKE.CORTEX.COMPLETE SQL function for generative AI tasks.
C) The Snowflake Cortex LLM REST API when invoking the '/api/v2/cortex/inference:complete' endpoint.
D) The 'SNOWFLAKCORTEX.TRY_COMPLETE SQL function, which is the error-tolerant version of 'COMPLETE.
E) The 'Cortex Playground' (Public Preview) when testing prompts and model settings.

3. A data engineer is setting up an automated pipeline to process incoming documents using Snowflake's AI_PARSE DOCUMENT function. To ensure the pipeline functions without errors, which of the following input requirements or limitations must be strictly adhered to?

A) To optimize performance, queries calling the Cortex AI_PARSE_DOCUMENT function should be executed in a large warehouse (e.g., XL or larger), as bigger warehouses increase processing speed for this function.
B) Supported file formats for processing include PDF, PPTX, DOCX, JPEG, JPG, PNG, TIFF, TIF, HTML, and TX T, covering a wide range of common document and image types.
C) The maximum file size for any document processed by is AI_PARSE_DOCUMENT 100 MB, and it can handle up to 300 pages per document.
D) All documents must be stored in an internal or external stage, but only internal stages support server-side encryption with the SNOWFLAKE_SSE type.
E) For HTML and TXT files, billing for AI_PARSE_DOCUMENT is based on each chunk of 3,000 characters, with the last chunk also being billed even if shorter.

4. A developer is integrating a Cortex Fine-tuning pipeline into an automated data workflow and needs to ensure structured outputs and monitor the process effectively. They are also aware of certain architectural limitations within Snowflake. Which of the following statements regarding advanced usage or limitations of Snowflake Cortex Fine-tuning and related LLM functions are accurate? (Select all that apply)

A) To achieve the most consistent and deterministic results from a fine-tuned LLM during inference, the temperature option in the
B) After initiating a fine-tuning job using the
C) The regional availability of the
D) Fine-tuning jobs, being long-running processes, are inherently tied to the worksheet session where they are initiated, and terminating the session will interrupt the training.
E) Snowflake Cortex Fine-tuning functions inherently support dynamic tables as sources for training data, allowing for automated and continuous re-training as the underlying data evolves.

5. A data engineering manager needs to audit Cortex LLM function costs to identify specific SQL queries that are unexpectedly high in token consumption for the 'llama3.1-8b' model. They require granular analysis of prompt, completion, and guardrail token usage for these queries. Which of the following Snowflake methods or views would provide the necessary insights?