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Snowflake GES-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Snowflake Document AI	12%	- Document preparation and processing - Document AI setup and configuration - Performance optimization and troubleshooting - Data extraction and structured output
Topic 2: Snowflake Gen AI Governance	22%	- Guardrails, safety controls, and bias mitigation - AI governance framework and policies - Monitoring, logging, and observability - Cost management and token-based pricing - Audit and compliance for AI workloads
Topic 3: Snowflake Gen AI & LLM Functions	40%	- RAG implementation in Snowflake - Embedding functions: EMBED_TEXT_*, vector storage and similarity search - API integration and authentication - Model deployment with Snowpark Container Services and Model Registry - Cortex LLM functions: COMPLETE, CLASSIFY_TEXT, EXTRACT_ANSWER, SENTIMENT, SUMMARIZE, TRANSLATE
Topic 4: Snowflake for Gen AI Overview	26%	- Role-based access control (RBAC) for AI resources - Snowflake Gen AI principles and best practices - Snowflake Copilot and AI assistant capabilities - Cortex AI components: Cortex Search, Cortex Analyst, Cortex LLMs

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data scientist needs to generate vector embeddings for product descriptions stored in a column 'PRODUCT_DESCRIPTION' in the 'PRODUCT_CATALOG' table. They want to use the 'e5-base-v2 model for this task. Which of the following SQL statements correctly applies the 'SNOWFLAKE.CORTEX.EMBED TEXT 768' function and accurately describes the expected data type of the resulting embedding?

A) The query

B) The query

C) The query

D) The query

E) The query

2. A project team is preparing to deploy a Document AI solution to process scanned customer feedback forms. They have created a dedicated role, 'customer feedback _ processor', and successfully granted it the SNOWFLAKE. DOCUMENT_INTELLIGENCE_CREATOR database role. The environment consists of 'feedback database, 'forms schema' schema, and 'ai workload warehouse. However, when the attempts to prepare a Document AI model build in Snowsight, they encounter a 'permission denied' error. Which of the following missing 'USAGE' grants could be the direct cause of this error?

A)

B)

C)

D)

E)

3. A data engineer is developing a Snowflake Cortex LLM application that processes sensitive customer feedback. To ensure that generated responses from the 'COMPLETE' function are filtered for potentially unsafe or harmful content, they need to enable Cortex Guard. Which of the following SQL 'COMPLETE function calls correctly demonstrates the enablement of Cortex Guard with the default unsafe response message?

A)

B)

C)

D)

E)

4. A data scientist is optimising a Cortex Analyst application to improve the accuracy of literal searches within user queries, especially for high-cardinality dimension values. They decide to integrate Cortex Search for this purpose. Which of the following statements are true about this integration and the underlying data types in Snowflake? (Select all that apply)

A) The cost for embedding data into a Cortex Search Service is primarily incurred per output token generated by the embedding model, as these represent the final vector embeddings, rather than input tokens.
B) For optimal RAG retrieval performance with Cortex Search, it is generally recommended to split text into chunks of no more than 512 tokens, even when using embedding models with larger context windows such as 'snowflake-arctic-embed-l-v2.0-8k'.
C) The "VECTOR data type in Snowflake, used to store embeddings generated for Cortex Search, is fully supported as a clustering key in standard tables and as a primary key in hybrid tables to accelerate vector similarity searches.
D) Cortex Search Services, when configured as a source for Snowflake dynamic tables, automatically refresh their search index with continuous data updates, maintaining low-latency search results.
E) To integrate Cortex Search with a logical dimension, the semantic model YAML must include a block within the dimension's definition, specifying the service name and optionally a 'literal_column' .