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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Model Evaluation & Responsible AI | - Evaluation metrics for LLM outputs - Bias, fairness, and explainability considerations |
| Snowflake AI & Cortex | - Snowflake Cortex capabilities - AI functions and services in Snowflake |
| Generative AI Fundamentals | - Model capabilities and limitations - Core concepts of generative AI and LLMs |
| Data Governance & Security | - Responsible use of AI in enterprise environments - Data privacy and access controls |
| Embeddings, Vector Search & RAG | - Vector search in Snowflake ecosystem - Embeddings fundamentals - Retrieval-Augmented Generation (RAG) workflows |
| Use Cases & Solution Design | - End-to-end GenAI solution architecture - Enterprise AI application patterns in Snowflake |
| Prompt Engineering | - Optimization of prompts for LLM outputs - Prompt design techniques |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
1. A company is developing a RAG application to provide concise and highly relevant answers to user queries from a vast knowledge base of technical documents. They are using Cortex Search for retrieval and are considering different embedding models and text chunking strategies to optimise the system. Which of the following statements about Cortex Search embedding models and RAG best practices are correct? (Select all that apply)
A) The cost for embedding models in Cortex Search, such as 'snowflake-arctic-embed-l-v2.0' and 'e5-base-v2, is incurred based on both input and output tokens.
B) For optimal RAG retrieval quality with Cortex Search, it is recommended to split text into chunks of no more than 512 tokens, even when using models with larger context windows like 'snowflake-arctic-embed-l-v2.0-8k'.
C) Using the 'snowflake-arctic-embed-l-v2.0-8k' model, which has an 8192-token context window, allows processing entire large technical documents as a single chunk for embedding, leading to better RAG results by preserving full document context.
D) The
E) The 'voyage-multilingual-2 model is suitable for multilingual documents and has a significantly larger context window (32000 tokens) compared to 'snowflake- arctic-embed-l-v2.C (512 tokens), making it more robust for longer text inputs.
2. A data application developer is tasked with building a multi-turn conversational AI application using Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To ensure the conversation flows naturally and the LLM maintains context from previous interactions, which of the following is the most appropriate method for handling and passing the conversation history?
A) Option B
B) Option A
C) Option C
D) Option E
E) Option D
3. A data engineering team is setting up a pipeline to automatically process various document types using AI_PARSE_DOCUMENT from an internal stage. Before writing any SQL, they need to ensure their Snowflake environment and the role they will use have the necessary permissions and configurations. Which of the following statements correctly describe essential prerequisites or access control requirements for successfully using AI_PARSE_DOCUMENT in this setup?
A) Option B
B) Option A
C) Option C
D) Option E
E) Option D
4. A data team is refining their Cortex Analyst semantic model to improve the accuracy of responses for specific, frequently asked questions and to enable better literal value searches. Consider a semantic model being developed to address these requirements. Which two configurations or features are directly relevant and correctly applied in the semantic model YAML for these purposes?
A) Option B
B) Option A
C) Option C
D) Option E
E) Option D
5. A Gen AI Specialist is tasked with implementing a data pipeline to automatically enrich new customer feedback entries with sentiment scores using Snowflake Cortex functions. The new feedback arrives in a staging table, and the enrichment process must be automated and cost-effective. Given the following pipeline components, which combination of steps is most appropriate for setting up this continuous data augmentation process?
A) Option B
B) Option A
C) Option C
D) Option E
E) Option D
Solutions:
| Question # 1 Answer: B,D,E | Question # 2 Answer: C | Question # 3 Answer: A,B,D | Question # 4 Answer: A,B | Question # 5 Answer: C |
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