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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation for Gen AI | 15-20% | - Data governance for AI workloads - Unstructured data handling - Document processing and chunking strategies - Vector stores and embeddings in Snowflake |
| Topic 2: Architecture and Best Practices | 10-15% | - LLM pipeline architecture design - Security and privacy considerations - Cost management strategies - Performance optimization techniques - Monitoring and evaluation frameworks |
| Topic 3: Generative AI Fundamentals and Concepts | 20-25% | - Prompt engineering principles - Vector embeddings and similarity search - Retrieval-Augmented Generation (RAG) concepts - Fine-tuning vs. retrieval approaches - LLM fundamentals and architectures |
| Topic 4: Snowflake Cortex AI Capabilities | 25-30% | - Model selection and cost optimization - Cortex AI functions and features - Snowflake Copilot integration - Secure data handling in AI workflows - COMPLETE function usage and parameters |
| Topic 5: Cortex Analyst and Semantic Layer | 20-25% | - Business logic implementation in semantic models - Performance tuning for analytical queries - Text-to-SQL translation and optimization - Semantic model design and configuration |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
A Gen AI engineer is tasked with selecting the most suitable Large Language Model (LLM) from Snowflake Cortex AI for a new customer service chatbot. They need to rapidly prototype and compare different LLMs with varying parameters on a sample dataset before committing to a production deployment. Which of the following statements accurately describe how the Cortex Playground (Public Preview) can assist in this scenario?
- A. It enables side-by-side comparison of model outputs for different LLMs and model settings, facilitating an informed decision on model selection.
- B. It allows direct fine-tuning of selected LLMs with custom datasets within the playground interface to improve model performance for specific tasks.
- C. It supports exporting the tested prompts and model configurations as Python code, ready for integration into a Snowpark ML pipeline.
- D. It provides a mechanism to deploy the chosen LLM directly into Snowpark Container Services (SPCS) compute pools from within the playground for immediate production use.
- E. It allows connection to a Snowflake table with textual data, processing up to 100 rows, to experiment with prompts directly on actual data.
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A financial institution uses Snowflake Cortex Analyst with strict role-based access control (RBAC) on their Snowflake-hosted LLMs. The security team has granted specific 'CORTEX-MODEL-ROLE application roles to different analyst teams, ensuring they only access approved models. A new requirement arises to enable Azure OpenAI GPT models for Cortex Analyst to leverage a specific feature. An administrator proceeds to execute:
Which of the following statements accurately describe the implications of this change?
- A. Option B
- B. Option A
- C. Option D
- D. Option C
- E. Option E
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A data analyst is using Snowflake Copilot in Snowsight to generate SQL queries for a new dataset containing customer PII. Which of the following statements accurately describes how Snowflake Copilot operates with respect to data access, governance, and model interaction?
- A. Snowflake Copilot directly accesses and processes the raw data within customer tables to understand its content and generate SQL.
- B. To protect sensitive information, Snowflake Copilot transmits sampled PII data to an external LLM for schema understanding before generating SQL.
- C. While Snowflake Copilot generates SQL based on metadata, the generated SQL queries are executed in an isolated environment that does not respect existing Snowflake RBAC policies.
- D. Snowflake Copilot is powered by a fine-tuned model that runs securely inside Snowflake Cortex, leveraging only database/schema/table/column names and data types, ensuring data remains within Snowflake's governance boundary and respects RBAC.
- E. Snowflake Copilot requires explicit column-level grants for direct data access, similar to how a human analyst would query specific data points.
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An AI engineer is building an automated pipeline in Snowflake that processes various types of textual data using Cortex AI functions. To ensure the pipeline's stability and avoid failures due to exceeding LLM context windows, they integrate SNOWFLAKE.CORTEX.COUNT_TOKENS and TRY_COMPLETE
. Consider the following code snippets and statements about context window management in Snowflake Cortex.
- A. Option B
- B. Option A
- C. Option D
- D. Option C
- E. Option E
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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. 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.
- B. Configuring a custom instruction with a short, precise task description to reduce the input token count for the LLMs orchestrating SQL generation.
- C. 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.
- D. 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.
- E. 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.
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