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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake Cortex AI Capabilities | 25-30% | - Snowflake Copilot integration - Secure data handling in AI workflows - COMPLETE function usage and parameters - Cortex AI functions and features - Model selection and cost optimization |
| Topic 2: Generative AI Fundamentals and Concepts | 20-25% | - Retrieval-Augmented Generation (RAG) concepts - Fine-tuning vs. retrieval approaches - Vector embeddings and similarity search - LLM fundamentals and architectures - Prompt engineering principles |
| Topic 3: Architecture and Best Practices | 10-15% | - LLM pipeline architecture design - Security and privacy considerations - Performance optimization techniques - Cost management strategies - Monitoring and evaluation frameworks |
| Topic 4: Data Preparation for Gen AI | 15-20% | - Data governance for AI workloads - Vector stores and embeddings in Snowflake - Document processing and chunking strategies - Unstructured data handling |
| Topic 5: Cortex Analyst and Semantic Layer | 20-25% | - Text-to-SQL translation and optimization - Semantic model design and configuration - Performance tuning for analytical queries - Business logic implementation in semantic models |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
A Snowflake account administrator in an Azure East US 2 region needs to enable users to access a new, highly capable LLM, 'claude-3-5-sonnet' , which is currently only natively available in AWS regions via Snowflake Cortex. The administrator also wants to ensure that only specific, approved LLMs can be used across the organization. Which configuration steps are necessary for the administrator to achieve these requirements?
- A. Set the account parameter to include 'claude-3-5-sonnet' , and then set the account parameter to 'TRUE to allow cross-region inference for all Cortex features.
- B. The 'CORTEX ENABLED CROSS REGION' parameter allows access to models in other regions, but access to specific LLMs is controlled solely by individual user privileges granted directly on the model objects, not by an account-level allowlist.
- C. Create a 'COMPUTE POOL' with a 'GPU NV_S instance family in Azure East US 2, and then deploy a custom PyCaret model of 'claude-3-5-sonnet' to this pool through the Snowflake Model Registry.
- D. Since 'claude-3-5-sonnet' is an OpenAl model, the administrator must enable the 'ENABLE_CORTEX ANALYST MODEL AZURE OPENAI' account parameter, and then the model will automatically be available for cross-region inference without further action.
- E. Grant the 'SNOWFLAKE.CORTEX_USER database role to the relevant user roles. Set the account parameter to 'ANY REGION' or a list including an AWS region where 'claude-3-5-sonnet' is natively available. Additionally, configure the 'CORTEX MODELS ALLOWLIST to explicitly permit 'claude-3-5-sonnet' and other desired models.
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A data engineering team needs to configure their Snowflake environment to process documents using AI_PARSE_DOCUMENT and generate text embeddings using EMBED_TEXT_1024 with the voyage-multilingual-2 model. Their Snowflake account is in a region where these specific capabilities or models are only available via cross-region inference. The team needs to ensure these functions work correctly without constant region-specific model selection. Which of the following is the correct configuration action and an important consideration?
- A. Option C
- B. Option D
- C. Option E
- D. Option A
- E. Option B
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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 C
- B. Option D
- C. Option E
- D. Option A
- E. Option B
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An 'ACCOUNTADMIN' has configured the 'CORTEX MODELS ALLOWLIST parameter to allow only the 'mistral-large? model. A developer, whose role has been granted 'SNOWFLAKE.CORTEX USER and the specific application role 'SNOWFLAKE."CORTEX- MODEL-ROLE-LLAMA3.1-70B"' , subsequently accesses the Cortex LLM Playground. Which models would be available for selection and successful inference by this user within the Playground?
- A. Option C
- B. Option D
- C. Option E
- D. Option A
- E. Option B
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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. The
- B. For optimal search results with Cortex Search, source text should be pre-split into chunks of no more than 512 tokens, even when using models with larger context windows like
- C. Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
- D. To create a Cortex Search Service, one must explicitly specify an embedding model and manually manage its underlying infrastructure, similar to deploying a custom model via Snowpark Container Services.
- E. Enabling change tracking on the source table for the Cortex Search Service is optional; the service will still refresh automatically even if change tracking is disabled.
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