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Snowflake SnowPro® Specialty: Gen AI Certification : GES-C01 Exam
- Exam Code: GES-C01
- Exam Name: SnowPro® Specialty: Gen AI Certification Exam
- Updated: Sep 10, 2026
- Q & A: 351 Questions and Answers
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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation for Gen AI | 15-20% | - Unstructured data handling - Document processing and chunking strategies - Vector stores and embeddings in Snowflake - Data governance for AI workloads |
| Topic 2: Snowflake Cortex AI Capabilities | 25-30% | - Model selection and cost optimization - Cortex AI functions and features - COMPLETE function usage and parameters - Snowflake Copilot integration - Secure data handling in AI workflows |
| Topic 3: Architecture and Best Practices | 10-15% | - Cost management strategies - LLM pipeline architecture design - Security and privacy considerations - Performance optimization techniques - Monitoring and evaluation frameworks |
| Topic 4: Cortex Analyst and Semantic Layer | 20-25% | - Performance tuning for analytical queries - Business logic implementation in semantic models - Text-to-SQL translation and optimization - Semantic model design and configuration |
| Topic 5: Generative AI Fundamentals and Concepts | 20-25% | - LLM fundamentals and architectures - Retrieval-Augmented Generation (RAG) concepts - Fine-tuning vs. retrieval approaches - Vector embeddings and similarity search - Prompt engineering principles |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
Question #1
A data scientist wants to fine-tune a
mistral -7b
model to improve its ability to generate specific product descriptions based on brief input features. They have a table named PRODUCT_CATALOG with columns PRODUCT_FEATURES (text) and GENERATED_DESCRIPTION (text). Which of the following statements correctly describe the preparation and initiation of this fine-tuning job in Snowflake Cortex?
(Select all that apply)
- A. The SQL query for the training data must select columns aliased as
- B. The
- C. Once a fine-tuned model is created, it is fully managed by the Snowflake Model Registry API, allowing for programmatic updates to its parameters and versions.
- D. The fine-tuning job must be created using a
- E. O To generate highly structured
Correct Answer: A,E 🗳️
Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).
Question #2
An analytics team needs to use various Snowflake Cortex LLM functions and wants to understand the cost implications and governance mechanisms for controlling model access and usage. The "ACCOUNTADMIN' has restricted general LLM access using the 'CORTEX_MODELS_ALLOWLIST' parameter. The team is particularly interested in how model complexity and specific function calls impact billing and how access can be managed effectively for different user groups.
- A. Option B
- B. Option D
- C. Option E
- D. Option A
- E. Option C
Correct Answer: C,D,E 🗳️
Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).
Question #3
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. Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
- B. The
- C. 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
- 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. 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.
Correct Answer: A,B,C 🗳️
Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).
Question #4
A data analytics team is building a Retrieval Augmented Generation (RAG) application to provide contextual answers from a vast repository of internal documents stored in Snowflake. They are evaluating different strategies for generating and retrieving text embeddings to optimize the overall RAG pipeline's performance and relevance. Which of the following statements accurately describe performance considerations related to embedding generation and retrieval in this RAG context? (Select all that apply)
- A. Option B
- B. Option D
- C. Option E
- D. Option A
- E. Option C
Correct Answer: A,E 🗳️
Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).
Question #5
A multi-national corporation uses Snowflake across several AWS regions. Their primary operational Snowflake account is in AWS US East (Ohio), but they need to leverage a specific AI_COMPLETE model, llama4-maverick, which is natively available in AWS US East 1 (N. Virginia) but not in US East (Ohio). To address this, the Snowflake administrator enables cross-region inference for their US East (Ohio) account.
- A. Option B
- B. Option D
- C. Option E
- D. Option A
- E. Option C
Correct Answer: B,D 🗳️
Explanation: Only visible for RealExamFree members. You can sign-up / login (it's free).
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