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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake Document AI | 12% | - Data extraction and structured output - Performance optimization and troubleshooting - Document preparation and processing - Document AI setup and configuration |
| Topic 2: Snowflake Gen AI Governance | 22% | - Cost management and token-based pricing - Audit and compliance for AI workloads - Guardrails, safety controls, and bias mitigation - AI governance framework and policies - Monitoring, logging, and observability |
| Topic 3: Snowflake for Gen AI Overview | 26% | - Snowflake Copilot and AI assistant capabilities - Snowflake Gen AI principles and best practices - Role-based access control (RBAC) for AI resources - Cortex AI components: Cortex Search, Cortex Analyst, Cortex LLMs |
| Topic 4: Snowflake Gen AI & LLM Functions | 40% | - API integration and authentication - Cortex LLM functions: COMPLETE, CLASSIFY_TEXT, EXTRACT_ANSWER, SENTIMENT, SUMMARIZE, TRANSLATE - RAG implementation in Snowflake - Model deployment with Snowpark Container Services and Model Registry - Embedding functions: EMBED_TEXT_*, vector storage and similarity search |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
- A. Option A
- B. Option E
- C. Option B
- D. Option D
- E. Option C
Correct Answer: A,E 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
A data engineering team is tasked with creating vector embeddings for a collection of diverse, multilingual research papers for a semantic search application. They need to use 'SNOWFLAKE.CORTEX.EMBED TEXT 1024' and are considering two models: 'snowflake-arctic-embed-l-v2.0' and 'voyage-multilingual-2'. They also need to ensure the resulting embeddings are stored correctly and understand potential text truncation. Which of the following statements correctly describes the application of the 'EMBED TEXT 1024' function for these models and the characteristics of the generated embeddings?
- A. Using either 'snowflake-arctic-embed-l-v2.0' or 'voyage-multilingual-2' with will result in a 'VECTOR(FLOAT, 1024)' data type, which is compatible with 'VARIANT columns.
- B. The function call
- C. The query
- D. The 'snowflake-arctic-embed-I-v2.0' model has a larger context window than 'voyage-multilingual-2 , making it more suitable for longer research papers without truncation.
- E. Both 'snowflake-arctic-embed-l-v2.0' and 'nv-embed-qa-4' are multilingual models with a 1024-dimension output, making them interchangeable for this use case.
Correct Answer: C 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
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. 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.
- 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. The
- D. 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.
- E. Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
Correct Answer: B,C,E 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
An ML engineer is developing a RAG application in Python and wants to use the TruLens SDK to trace the distinct phases of its execution, specifically the context retrieval and answer generation steps. They aim to clearly differentiate the tracing of the function responsible for retrieving context.
- A.
- B.
- C.
- D.
- E.
Correct Answer: E 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
A data engineering team is developing a Cortex Analyst semantic model in YAML for an e-commerce platform. They need to ensure high accuracy for common queries, improve literal matching for product names, and understand limitations for supported data types. Which of the following statements correctly describe aspects of semantic model configuration or capabilities for these requirements?
- A. Option A
- B. Option E
- C. Option B
- D. Option D
- E. Option C
Correct Answer: A,E 🗳️
Explanation: Only visible for Exam-Killer members. You can sign-up / login (it's free).
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