Free Practice Questions for Snowflake GES-C02 Certification
Study with 351 exam-style practice questions designed to help you prepare for the Snowflake SnowPro Specialty: Gen AI (GES-C02). All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.
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Key information about Snowflake SnowPro Specialty: Gen AI (GES-C02)
- Multiple choice
GES-C02
Consistent with SnowPro certification standards
No
May 19, 2026
55
2 years
July 20, 2026
May 15, 2026
May 19, 2026 (GES-P02)
Exam Topics & Skills Assessed
Skills measured (from the official study guide)
Domain 1: Snowflake for Gen AI Overview
Subdomain 1.1: Define Snowflake’s Gen AI principles and features.
• Snowflake Cortex - Cortex Models and Functions - Cortex Fine-tuning (Public Preview) - Cortex Search - RAG use cases - Unstructured data use cases - Cortex Analyst - Text-to-SQL use cases - Cortex Agents
• Snowflake Cortex Code
• Cortex Code in Snowsight UI - Cortex Code Command Line (CLI)
• Snowflake Copilot Inline (Public Preview) - Cortex Models and Functions - Cortex Fine-tuning (Public Preview) - Cortex Search - RAG use cases
• Snowflake Intelligence
• Different interfaces - AI Studio - SQL - REST API
• Bringing your own models into Snowflake - Snowflake Model Registry (custom model) - Snowpark Container Services
Subdomain 1.2: Outline Gen AI capabilities in Snowflake.
• Prompting
• Cortex AI functions - Vector-embedding - Context Windows
• Cortex Search - Multi-index queries - Access control requirements - Different ways to use Cortex Search
• Cortex Analyst - Semantic Views - Semantic Views Autopilot - YAML Specification for Semantic Views - Verified Query - Custom Instructions
• Cortex Agents
• Snowflake Intelligence
• Cross-region inference - CORTEX_ENABLED_CROSS_REGION parameter - Considerations (e.g., latency, availability)
• REST APIs
• Model Context Protocol (MCP)
• Snowflake Cortex Code - Cortex Code CLI commands
• Cortex Knowledge Extensions (CKE)
Domain 2: Snowflake Gen AI Functions
Subdomain 2.1: Apply AI functions in Snowflake.
• Snowflake Cortex AI functions - General - AI_COMPLETE - COMPLETE Structured Outputs - Task-specific functions - AI_CLASSIFY - AI_EXTRACT - AI_PARSE_DOCUMENT - AI_SENTIMENT - SUMMARIZE - AI_SUMMARIZE_AGG - AI_TRANSLATE - AI_EMBED - AI_FILTER - AI_AGG - AI_SIMILARITY - AI_TRANSCRIBE - AI_REDACT - Vector functions - VECTOR_INNER_PRODUCT - VECTOR_L1_DISTANCE - VECTOR_L2_DISTANCE - VECTOR_COSINE_SIMILARITY - VECTOR_TRUNCATE - VECTOR_NORMALIZE - VECTOR_SUM - VECTOR_MIN - VECTOR_MAX - VECTOR_AVG - Helper functions - AI_COUNT_TOKENS - TRY_COMPLETE - SPLIT_TEXT_RECURSIVE_CHARACTER - SPLIT_TEXT_MARKDOWN_HEADER - TO_FILE - PROMPT
Subdomain 2.2: Perform data analysis given a use case.
• Use fully-managed LLMs, RAG, and text-to-SQL services - Unstructured data - Functions - AI_PARSE_DOCUMENT - AI_EXTRACT - AI_SIMILARITY - AI_COMPLETE - Cortex Search - Recursive split text markdown - Chunk sizing - Embedding models - Semantic reranking - Multi-modal Analytics - Audio and Image Processing - Structured data - Functions - AI_COMPLETE - Cortex Analyst - Cortex Analyst Verified Query Repository (VQR) - Integration with Cortex Search - Suggested Questions - CUSTOM_INSTRUCTIONS - Performance considerations - Choosing a model - Latency (e.g., model size) - Accuracy (e.g., fine-tuning, reducing hallucinations) - Model capability - Provisioned Throughput
Subdomain 2.3: Build or interact with interfaces to chat with data in Snowflake.
• Set up the Snowflake environment - Required privileges
• Invoke Cortex functions within the application code (e.g., Streamlit in Snowflake) - Chat conversations - Multi-turn architecture - Update parameters (i.e., messages array for conversation history)
• Snowflake Intelligence
Subdomain 2.4: Apply Snowflake Cortex functions in data pipelines.
• Snowflake Cortex
• SQL interface
• Data extraction
• Data enrichment
• Data augmentation
• Data transformations
Subdomain 2.5: Run third-party models in Snowflake.
• Using Snowpark Container Services - Environment setup - Docker images - Specification files - Create compute pool - Create image repository
• Using Snowflake Model Registry - Logging the model - Calling the model
Domain 3: Snowflake Gen AI Governance
Subdomain 3.1: Set up model access controls.
• Limits on which models can be used - Restrict access to specific models
• Application roles - Control model access
• Role-Based Access Control (RBAC)
• Account-level allowlist parameter
• Data safety and security considerations - Cross region inference - Guardrails - Sensitive data management (e.g., AI_REDACT ) - Methods to reduce model hallucinations and bias
• REST API authentication methods
Subdomain 3.2: Grant and revoke Role-Based Access Control (RBAC) and privileges.
• Individual privileges - Specific requirements for Analyst, Search, Agents, and Snowflake Intelligence
• Roles - CORTEX_USER - CORTEX_ANALYST_USER - CORTEX_AGENT_USER - CORTEX_EMBED_USER
Subdomain 3.3: Manage, monitor, and optimize Snowflake Cortex costs.
• Cortex Agents - Limit token usage
• Cortex Search - Different types of costs (e.g., virtual warehouse, EMBED_TEXT, serving, indexing)
• Cortex Analyst
• Cortex AI functions - Minimize tokens - Token cost implications
• Tracking costs of Snowpark Container Services - Compute pools
• Tracking model usage and consumption - Usage quotas - CORTEX_ANALYST_USAGE_HISTORY - CORTEX_AISQL_USAGE_HISTORY - CORTEX_SEARCH_DAILY_USAGE_HISTORY - CORTEX_REST_API_USAGE_HISTORY - CORTEX_PROVISIONED_THROUGHPUT_USAGE_HISTORY - METERING_DAILY_HISTORY - METERING_HISTORY
• Object tagging to monitor AI services costs
Subdomain 3.4: Use Snowflake AI observability tools.
• Snowflake AI observability features - Evaluation metrics - Comparisons - Tracing - Logging - Event tables
• Implementation methods - Trulens SDK
Domain 4: Snowflake Document Processing
Subdomain 4.1: Use document parsing functions.
• AI_PARSE_DOCUMENT - OCR mode - LAYOUT mode - page_split - page_limit
• AI_EXTRACT - Response format - How to prompt/Prompt engineering
Subdomain 4.2: Prepare and manage documents and implement extracting workflows.
• Upload documents
• Requirements (e.g., formats, size limits)
Subdomain 4.3: Build automated document processing pipelines with Cortex AI integration.
• Orchestration of Snowflake tooling - Streams - Tasks
Subdomain 4.4: Troubleshoot and optimize document processing.
• Extracting query errors - GET_PRESIGNED_URL function
• Requirements and privileges
• Cost and best practice considerations
• Fine-tuning arctic-extract models
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