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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Architecture and Best Practices | 10-15% | - Monitoring and evaluation frameworks - Security and privacy considerations - Cost management strategies - Performance optimization techniques - LLM pipeline architecture design |
| Generative AI Fundamentals and Concepts | 20-25% | - Fine-tuning vs. retrieval approaches - Vector embeddings and similarity search - Retrieval-Augmented Generation (RAG) concepts - LLM fundamentals and architectures - Prompt engineering principles |
| Snowflake Cortex AI Capabilities | 25-30% | - Model selection and cost optimization - COMPLETE function usage and parameters - Snowflake Copilot integration - Secure data handling in AI workflows - Cortex AI functions and features |
| Cortex Analyst and Semantic Layer | 20-25% | - Semantic model design and configuration - Text-to-SQL translation and optimization - Business logic implementation in semantic models - Performance tuning for analytical queries |
| Data Preparation for Gen AI | 15-20% | - Data governance for AI workloads - Unstructured data handling - Vector stores and embeddings in Snowflake - Document processing and chunking strategies |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
1. A data engineering team is building a pipeline to process legal documents using Snowflake Cortex functions. They aim to extract specific entities and summarize key clauses while being highly cost-conscious. To optimize token-based costs, which of the following practices should they implement when using Cortex LLM functions?
A) Option D
B) Option B
C) Option C
D) Option E
E) Option A
2. A data architect is designing a workflow to programmatically extract highly structured data from various text inputs using Snowflake Cortex AI's AI_COPIPLETE function through its REST API. They require the output to strictly adhere to a complex JSON schema for downstream processing and need to manage associated costs. Which of the following statements accurately describe aspects of this approach?
A) Option D
B) Option B
C) Option C
D) Option E
E) Option A
3. 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 D
B) Option B
C) Option C
D) Option E
E) Option A
4. A Gen AI Specialist is setting up their Snowflake environment to deploy a high-performance open-source LLM for real-time inference using Snowpark Container Services (SPCS). They need to create a compute pool that can leverage NVIDIAAIOG GPUs to optimize model performance. Which of the following SQL statements correctly creates a compute pool capable of supporting an intensive GPU usage scenario, such as serving LLMs, while adhering to common configuration best practices for a new, small-scale deployment in Snowpark Container Services?
A) 
B) 
C) 
D) 
E) 
5. A data scientist is implementing a Retrieval Augmented Generation (RAG) system in Snowflake for a legal document repository. They need to convert legal document chunks into vector embeddings and efficiently find the most relevant document chunks based on a user's query. Which of the following statements accurately describe the process and best practices for creating and using these vector embeddings with Snowflake Cortex LLM functions?
A) Option D
B) Option B
C) Option C
D) Option E
E) Option A
Solutions:
| Question # 1 Answer: B,C | Question # 2 Answer: A,C,E | Question # 3 Answer: E | Question # 4 Answer: B | Question # 5 Answer: C,E |
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