Updated: Aug 06, 2026
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| Certification Vendor: | Snowflake |
| Exam Name: | SnowPro® Specialty: Gen AI Certification Exam |
| Exam Number: | GES-C01 |
| Real Exam Qty: | 55 |
| Exam Duration: | 85 minutes |
| Passing Score: | 750 (scaled 0–1000) |
| Certificate Validity Period: | 2 years |
| Related Certifications: | SnowPro Core SnowPro Associate: Platform |
| Exam Format: | Multiple-choice, Multiple-select, Interactive scenario-based |
| Available Languages: | English |
| Exam Price: | $225 USD |
| Recommended Training: | Snowflake Gen AI Training Official Study Guide |
| Exam Registration: | Pearson VUE Registration |
| Sample Questions: | Snowflake GES-C01 Sample Questions |
| Exam Way: | Online proctored (OnVUE) or onsite at Pearson VUE test centers |
| Pre Condition: | Active SnowPro Core or SnowPro Associate: Platform certification; 1+ year Gen AI experience with Snowflake; proficiency in SQL, Python and data engineering concepts |
| Official Syllabus URL: | https://learn.snowflake.com/en/certifications/snowpro-GenAI/ |
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Snowflake for Gen AI Overview | 26% | - Snowflake Gen AI principles and best practices - Role-based access control (RBAC) for AI resources - Cortex AI components: Cortex Search, Cortex Analyst, Cortex LLMs - Snowflake Copilot and AI assistant capabilities |
| Topic 2: Snowflake Gen AI Governance | 22% | - Cost management and token-based pricing - Guardrails, safety controls, and bias mitigation - AI governance framework and policies - Monitoring, logging, and observability - Audit and compliance for AI workloads |
| Topic 3: Snowflake Gen AI & LLM Functions | 40% | - API integration and authentication - Embedding functions: EMBED_TEXT_*, vector storage and similarity search - Model deployment with Snowpark Container Services and Model Registry - RAG implementation in Snowflake - Cortex LLM functions: COMPLETE, CLASSIFY_TEXT, EXTRACT_ANSWER, SENTIMENT, SUMMARIZE, TRANSLATE |
| Topic 4: Snowflake Document AI | 12% | - Performance optimization and troubleshooting - Data extraction and structured output - Document AI setup and configuration - Document preparation and processing |
1. An ML engineer is working on a Snowflake project that requires storing and comparing high-dimensional feature vectors extracted from customer interaction logs. They need to ensure the vector data type is correctly defined and then perform an inner product calculation.
Which of the following statements about defining and using the
VECTOR
data type and
VECTOR_INNER_PRODUCT
function in Snowflake SQL are correct? (Select all that apply)
A) When calling
B) Direct comparison operators like
C) The
D) The
E) To store a vector with 512 floating-point dimensions in a table, the correct SQL syntax for adding a column would be:
2. A data engineering team needs to configure their Snowflake environment to process documents using AI_PARSE_DOCUMENT and generate text embeddings using EMBED_TEXT_1024 with the voyage-multilingual-2 model. Their Snowflake account is in a region where these specific capabilities or models are only available via cross-region inference. The team needs to ensure these functions work correctly without constant region-specific model selection. Which of the following is the correct configuration action and an important consideration?
A) Option E
B) Option D
C) Option A
D) Option B
E) Option C
3. A data architect is designing a workflow to programmatically extract highly structured data from various text inputs using Snowflake Cortex AI's AI_COPIPLETE function through its REST API. They require the output to strictly adhere to a complex JSON schema for downstream processing and need to manage associated costs. Which of the following statements accurately describe aspects of this approach?
A) Option E
B) Option D
C) Option A
D) Option B
E) Option C
4. A business team using a Snowflake Cortex Analyst-powered chatbot reports that follow-up questions in multi-turn conversations are sometimes slow to process, impacting user experience. The development team wants to optimize for responsiveness while maintaining accuracy in SQL generation. Which of the following strategies directly addresses latency in multi-turn conversations within Cortex Analyst, considering its underlying mechanisms?
A) Implement an explicit LLM summarization agent within the semantic model to condense conversation history before it's passed to subsequent LLM calls.
B) Switch the underlying text-to-SQL LLM to a smaller model, such as
C) Increase the warehouse size used for Cortex Analyst queries to 'Large' to accelerate LLM inference.
D) Configure the semantic model to reset the conversation context after every three turns to limit token count.
E) Rely on
5. An operations manager is concerned about the governance and monitoring of a newly deployed Cortex Analyst application, especially regarding cost tracking and how multi-turn conversations are handled. Which statement accurately describes a feature for monitoring or managing a Cortex Analyst application?
A) Option E
B) Option D
C) Option A
D) Option B
E) Option C
Solutions:
| Question # 1 Answer: A,D,E | Question # 2 Answer: A,D | Question # 3 Answer: B,C,E | Question # 4 Answer: A | Question # 5 Answer: E |
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