Snowflake GES-C01 Exam Information and Actual Questions

  • Exam Code/Number: GES-C01
  • Exam Name/Title: SnowPro® Specialty: Gen AI Certification Exam
  • Certification Provider: Snowflake
  • Corresponding Certification: Snowflake Certification
  • Exam Questions: 351
  • Updated On: Jul 31, 2026

GES-C01
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Snowflake
GES-C01 Exam
SnowPro® Specialty: Gen AI Certification Exam

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All the information you need to pass Snowflake SnowPro® Specialty: Gen AI Certification GES-C01 exam and free practice exam verified by ExamDiscuss exam experts.

Snowflake GES-C01 Exam Overview:

Exam Name:SnowPro Specialty: Gen AI Certification Exam (GES-C01)
Exam Number:GES-C01
Available Languages:English
Passing Score:750/1000
Real Exam Qty:Approximately 65
Certificate Validity Period:2 years
Exam Format:Multiple choice, Multiple select
Related Certifications:SnowPro Core Certification
Exam Duration:115 minutes
Exam Price:$175 USD
Recommended Training:Snowflake University Training
Snowflake Documentation (Cortex & AI)
Exam Registration:Pearson VUE Snowflake Exams
Snowflake Certification Portal
Sample Questions:Snowflake GES-C01 Sample Questions
Exam Way:Online proctored exam via Pearson VUE and authorized test centers
Pre Condition:No strict prerequisite required; recommended: SnowPro Core Certification or equivalent Snowflake data platform experience.
Official Syllabus URL:https://www.snowflake.com/training-and-certification/

Snowflake GES-C01 Exam Syllabus Topics:

SectionObjectives
Model Evaluation & Responsible AI- Bias, fairness, and explainability considerations
- Evaluation metrics for LLM outputs
Prompt Engineering- Optimization of prompts for LLM outputs
- Prompt design techniques
Data Governance & Security- Data privacy and access controls
- Responsible use of AI in enterprise environments
Snowflake AI & Cortex- AI functions and services in Snowflake
- Snowflake Cortex capabilities
Use Cases & Solution Design- Enterprise AI application patterns in Snowflake
- End-to-end GenAI solution architecture
Generative AI Fundamentals- Core concepts of generative AI and LLMs
- Model capabilities and limitations
Embeddings, Vector Search & RAG- Retrieval-Augmented Generation (RAG) workflows
- Vector search in Snowflake ecosystem
- Embeddings fundamentals


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