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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Generative AI Fundamentals and Concepts | 20-25% | - Prompt engineering principles - Retrieval-Augmented Generation (RAG) concepts - LLM fundamentals and architectures - Fine-tuning vs. retrieval approaches - Vector embeddings and similarity search |
| Data Preparation for Gen AI | 15-20% | - Vector stores and embeddings in Snowflake - Unstructured data handling - Document processing and chunking strategies - Data governance for AI workloads |
| 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 Cortex AI Capabilities | 25-30% | - Cortex AI functions and features - Snowflake Copilot integration - COMPLETE function usage and parameters - Model selection and cost optimization - Secure data handling in AI workflows |
| Architecture and Best Practices | 10-15% | - Monitoring and evaluation frameworks - Cost management strategies - Performance optimization techniques - Security and privacy considerations - LLM pipeline architecture design |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
1. A company is implementing a Document AI solution to extract sensitive financial data from invoices. They plan to fine-tune the Document AI model (Arctic-TILT) and then manage this custom model within the Snowflake Model Registry. Which of the following statements correctly outlines the access control, data handling, and model management principles for this scenario?
A) Option C
B) Option D
C) Option A
D) Option E
E) Option B
2. An organization is planning to implement a new Retrieval Augmented Generation (RAG) application and has chosen Snowflake Cortex Search as its core retrieval engine. To effectively manage their budget, the finance and data teams need a clear understanding of the various cost components associated with deploying and operating a Cortex Search Service. Which of the following represent distinct cost categories directly attributable to the deployment and ongoing operation of a Snowflake Cortex Search Service?
A) Storage for the materialized source data and the optimized search index data structures within the Snowflake account.
B) Compute costs for LLM inference (e.g., SNOWFLAKE.CORTEX.COMPLETE) when the RAG application uses the retrieved context to generate responses.
C) Virtual warehouse compute used for refreshing the search service's index and processing base object changes.
D) ' Services compute specifically for generating vector embeddings of text data during the indexing and update processes.
E) Cloud Services compute for monitoring underlying base objects for changes to trigger search service refreshes.
3. An organization has implemented a strict governance policy where the 'ACCOUNTADMIN' has set the 'CORTEX MODELS ALLOWLIST' to only permit 'gemma-7b' and 'llama3.1-8b' models. A developer then executes the following SQL statements in a Snowflake worksheet using 'TRY COMPLETE (SNOWFLAKE.CORTEX)". Assuming no specific RBAC model object grants are in place for the developer's role, what would be the outcome of these queries? SELECT
A) The first and second queries will return completions, while the third will return 'NULL' due to potential resource constraints for larger models.
B) The first and third queries will return completions, but the second query will raise an error indicating an unauthorized model attempt.
C) All three queries will return because 'TRY COMPLETE' will always prioritize strict adherence to the allowlist and any model not explicitly listed is considered unavailable.
D) Only the first query will return a completion, as 'gemma-7W is the smallest and most readily available model, while the others will return 'NULL'.
E) The first query will return a completion, the second will return 'NULL' , and the third will return a completion.
4. A company wants to ingest and process scanned invoices and digitally-born contracts in Snowflake. They need to extract all text, preserving layout for contracts and just the text content for scanned invoices. Which AI_PARSE_DOCUMENT modes would be most appropriate for this scenario, and what is the primary purpose of the function itself?
A) Primary purpose is to generate new text. For contracts, use OCR mode; for invoices, use LAYOUT mode.
B) Primary purpose is to classify text. For contracts, use LAYOUT mode; for invoices, use OCR mode.
C) Primary purpose is to extract data and layout. For contracts, use LAYOUT mode; for invoices, use OCR mode.
D) Primary purpose is to translate text. Both document types should use LAYOUT mode.
E) Primary purpose is to summarize text. For contracts, use OCR mode; for invoices, use LAYOUT mode.
5. A data application developer is tasked with creating a multi-turn conversational AI application using Streamlit in Snowflake (SiS), which will leverage Snowflake Cortex LLM functions. Considering the core requirements for building such an interactive chat interface and the underlying Snowflake environment, which of the following actions is a fundamental step in setting up the application for stateful conversations?
A) Option C
B) Option D
C) Option A
D) Option E
E) Option B
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A,C,D,E | Question # 3 Answer: E | Question # 4 Answer: C | Question # 5 Answer: E |








