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Snowflake GES-C01 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Snowflake for Gen AI Overview | 26% | - Snowflake Copilot and AI assistant capabilities - Role-based access control (RBAC) for AI resources - Snowflake Gen AI principles and best practices - Cortex AI components: Cortex Search, Cortex Analyst, Cortex LLMs |
| Snowflake Document AI | 12% | - Performance optimization and troubleshooting - Document AI setup and configuration - Document preparation and processing - Data extraction and structured output |
| Snowflake Gen AI Governance | 22% | - Audit and compliance for AI workloads - AI governance framework and policies - Cost management and token-based pricing - Guardrails, safety controls, and bias mitigation - Monitoring, logging, and observability |
| Snowflake Gen AI & LLM Functions | 40% | - RAG implementation in Snowflake - Embedding functions: EMBED_TEXT_*, vector storage and similarity search - Cortex LLM functions: COMPLETE, CLASSIFY_TEXT, EXTRACT_ANSWER, SENTIMENT, SUMMARIZE, TRANSLATE - Model deployment with Snowpark Container Services and Model Registry - API integration and authentication |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
1. A data scientist, 'Dl DEV', has been granted the

Despite these grants, 'DI_DEV' still receives a 'permission denied' error when attempting to ''prepare'' the Document AI model build in Snowsight. Which 'single missing privilege' is the most likely direct cause of this specific error for Document AI model build preparation?
A)
B)
C)
D)
E) 
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 C
B) Option D
C) Option E
D) Option B
E) Option A
3. A data application developer is using the Snowflake Cortex COMPLETE function to power a multi-turn conversational AI application. They want to ensure responses are creative but not excessively long, adhere to a specific JSON structure, and are filtered for safety. Given the following SQL query snippet, which statements accurately describe the impact of the specified options?
A) Setting
B) Including a
C) The
D) The
E) For a multi-turn conversation, previous user prompts and model responses should be passed in the
4. An organization enforces strict LLM access control. The has configured 'CORTEX MODELS_ALLOWLIST ='mistral-large2" and executed 'CALL SNOWFLAKE.MODELS.CORTEX BASE MODELS REFRESH()'. A developer, whose role DEV ROLE has been granted 'SNOWFLAKE.CORTEX USER' and 'SNOWFLAKE."CORTEX-MODEL-ROLE-CLAUDE-3-5- SONNET"' , attempts to make a REST API call to 'api/v2/cortex/inference:complete' using 'claude-3-5-sonnet' as the model name in the request body. Which of the following statements are true regarding this scenario?
A) Option C
B) Option D
C) Option E
D) Option B
E) Option A
5. A company is planning to process a large volume of legal documents to generate summaries using SNOWFLAKE. CORTEX. SUMMARIZE. Given the scale, they are acutely focused on managing costs and optimizing performance. Which of the following statements are true regarding the cost and performance characteristics of using SNOWFLAKE. CORTEX. SUMMARIZE? (Select all that apply)
A) The context window for the SWIMARIZE function is 4,096 tokens, ensuring efficiency for short documents only.
B) The fixed billing rate for the SUMMARIZE function is 0.10 Credits per one million Tokens processed.
C) The SUWARIZE function is billed primarily based on the number of output tokens generated in the response, not input tokens.
D) For SUWARIZE, Snowflake adds an internal prompt to the user's input text, which increases the total input token count for billing purposes beyond the raw text length.
E) Snowflake recommends using a larger warehouse (e.g., L or XL) for SUMMARIZE function calls to significantly improve processing performance for high-volume tasks.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A,C,E | Question # 3 Answer: B,C,D | Question # 4 Answer: A,D | Question # 5 Answer: B,D |




