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Snowflake GES-C01

GES-C01

Exam Code: GES-C01

Exam Name: SnowPro® Specialty: Gen AI Certification Exam

Updated: Aug 11, 2026

Q&A Number: 351 Q&As

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Snowflake GES-C01 Exam Syllabus Topics:

SectionWeightObjectives
Cortex Analyst and Semantic Layer20-25%- Text-to-SQL translation and optimization
- Semantic model design and configuration
- Business logic implementation in semantic models
- Performance tuning for analytical queries
Data Preparation for Gen AI15-20%- Data governance for AI workloads
- Vector stores and embeddings in Snowflake
- Document processing and chunking strategies
- Unstructured data handling
Architecture and Best Practices10-15%- Performance optimization techniques
- LLM pipeline architecture design
- Security and privacy considerations
- Cost management strategies
- Monitoring and evaluation frameworks
Snowflake Cortex AI Capabilities25-30%- Secure data handling in AI workflows
- Snowflake Copilot integration
- Model selection and cost optimization
- Cortex AI functions and features
- COMPLETE function usage and parameters
Generative AI Fundamentals and Concepts20-25%- Retrieval-Augmented Generation (RAG) concepts
- Fine-tuning vs. retrieval approaches
- Prompt engineering principles
- Vector embeddings and similarity search
- LLM fundamentals and architectures

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. An operations team at a company is implementing a robust governance framework to monitor and optimize the costs associated with their Snowflake Cortex LLM function usage. They need to identify which functions are driving the highest token consumption and overall credit usage to pinpoint areas for cost reduction. Which of the following monitoring tools or methods are appropriate for gaining these insights into Cortex LLM function costs and token consumption?

A) Option A
B) Option E
C) Option C
D) Option D
E) Option B


2. An analytics team is preparing documents for a new Document AI model build to extract information from internal policy reviews. They have a variety of documents that they intend to upload to an internal stage for processing. The document list includes: (1) a 70 MB PDF with 100 pages, (2) a 45 MB DOCX with 150 pages, (3) a 30 MB PNG image, (4) a 60 MB TIFF image, and (5) a 20 MB HTML file. All documents are in English. Which of these documents would 'fail' to meet the direct input requirements for Document AI processing?

A) The 30 MB PNG image.
B) The 60 MB TIFF image.
C) The 20 MB HTML file.
D) The 70 MB PDF with 100 pages.
E) The 45 MB DOCX with 150 pages.


3. A financial analytics team is developing an application to extract specific, structured financial data (e.g., company name, revenue, profit margin) from various news articles using Snowflake Cortex LLM functions. They require the output to strictly conform to a predefined JSON schema and want to ensure robust error handling. Which of the following statements are crucial considerations for achieving this goal?

A) To guarantee that critical fields like 'company name' and 'revenue' are always extracted, these properties must be explicitly listed within the ' required' array of the JSON schema provided to AI_COMPLETE.
B) The AI_COMPLETE function should be used with the response_format argument, supplying a JSON schema object that defines the required structure, data types, and constraints for the output.
C) For enhanced reliability in production pipelines, the team should wrap their AI_COMPLETE calls within TRY_COMPLETE, as it returns a structured error object if the model fails to adhere to the schema, allowing for detailed debugging.
D) Setting the temperature option to 0 in the AI_COMPLETE call is essential for obtaining the most consistent and accurate structured JSON outputs, regardless of task complexity or model used.
E) The complexity of the JSON schema provided to AI_COMPLETE has no impact on compute costs, as only the input text and generated content tokens are billed.


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 A
B) Option E
C) Option C
D) Option D
E) Option B


5. A security administrator is implementing strict model access controls for Snowflake Cortex LLM functions, including those accessed via the Cortex REST API. By default, the 'SNOWFLAKE.CORTEX USER' database role is granted to the 'PUBLIC' role, allowing all users to call Cortex AI functions. To enforce a more restrictive access policy, the administrator revokes 'SNOWFLAKE.CORTEX USER from 'PUBLIC'. Which of the following actions must the administrator take to ensure specific roles can 'still' make Cortex REST API requests, and what are the implications?

A) The 'SNOWFLAKE.CORTEX USER database role must be granted directly to individual users who need access, as it cannot be granted to other account roles.
B) Only the role can make cortex REST API calls after revoking 'SNOWFLAKE.CORTEX_USER from 'PUBLIC', as this role inherently bypasses all other access controls.
C) The from 'SNOWFLAKCORTEX USER database role is only required for SQL functions, not for the Cortex REST API, so no further action is needed after revoking 'PUBLIC for REST API access.
D) Access for Cortex REST API is managed independently of database roles; a separate REST API key must be provisioned for each user or application.
E) The 'SNOWFLAKE.CORTEX USER database role must be granted to the specific account roles, and then these account roles must be granted to users. Additionally, the account parameter can be used to restrict which models are accessible.


Solutions:

Question # 1
Answer: A,B,D,E
Question # 2
Answer: B,D,E
Question # 3
Answer: A,B,D
Question # 4
Answer: A,E
Question # 5
Answer: E

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