Free Practice Questions for Snowflake GES-C01 Certification
Study with 329 exam-style practice questions designed to help you prepare for the Snowflake SnowPro Specialty: Gen AI (GES-C01). All questions are aligned with the latest exam guide and include detailed explanations to help you master the material.
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Exam Details
Key information about Snowflake SnowPro Specialty: Gen AI (GES-C01)
- Multiple choice
Through Snowflake Continuing Education (CE) program (eligible Instructor-Led Training Courses or earning an equivalent/higher-level SnowPro Certification)
Active SnowPro Associate: Platform or SnowPro Core Certification
Candidates with 1+ years of Gen AI experience with Snowflake in an enterprise environment, advanced Python proficiency, and assumed data engineering and SQL knowledge. Roles include AI/ML Engineers, Data Scientists, Data Engineers, Data Application Developers, Data Analysts with programming experience.
10 – 13 hours
2 years
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, features, and best practices.
• Snowflake Cortex • LLMs • Cortex Search • Cortex Analyst • Cortex Fine-tuning • Cortex Agents (Public Preview) • Snowflake Copilot • Security, privacy, access, and control principles • Role-Based Access Control (RBAC) • Guardrails • Required privileges • Cortex LLM Functions • Control model access • CORTEX_MODELS_ALLOWLIST parameter • Different interfaces • Cortex LLM Playground (Public Preview) • SQL • REST API • Different ways of bringing your own models into Snowflake (for example, from Hugging Face) • Using Snowflake Model Registry (custom model)
Subdomain 1.2: Outline Gen AI capabilities in Snowflake.
• Cortex LLM functions (for example, task-specific, general) • Vector-embedding • Fine-tuning • Cortex Search • RAG use cases • Unstructured data use cases • REST APIs • Cortex Analyst • Semantic model generation • Stored in YAML files in a stage • Stored natively in semantic views (Public Preview) • Structured/text-to-SQL use cases • REST APIs • Cortex Agents (Public Preview) • REST APIs • Cross-region inference • CORTEX_ENABLED_ CROSS_REGION parameter • Considerations (for example, latency, availability) • Using Snowpark Container Services
Domain 2: Snowflake Gen AI & LLM Functions
Subdomain 2.1: Apply Gen AI and LLM functions in Snowflake.
• Snowflake Cortex • General • COMPLETE • COMPLETE Structured Outputs • Task-specific functions • CLASSIFY_TEXT • EXTRACT_ANSWER • PARSE_DOCUMENT • SENTIMENT • SUMMARIZE • TRANSLATE • EMBED_TEXT_768 • EMBED_TEXT_1024 • Cortex Search • Cortex Analyst • Cortex Fine-tuning • Cortex Agents (Public Preview) • Vector functions • VECTOR_INNER_ PRODUCT • VECTOR_L1_DISTANCE • VECTOR_L2_DISTANCE • VECTOR_COSINE_ SIMILARITY • Helper functions • COUNT_TOKENS • TRY_COMPLETE • SPLIT_TEXT_ RECURSIVE_CHARACTER • Choosing a model • Considerations (e.g. capability, latency, and cost)
Subdomain 2.2: Perform data analysis given a use case.
• Use fully-managed LLMs, RAG, and text-to-SQL services • Unstructured data • CORTEX PARSE_DOCUMENT • Structured data • Cortex Analyst • Cortex Analyst Verified Query Repository (VQR) • Integration with Cortex Search • Suggested Questions • Custom_ instructions field • Performance considerations • Latency (for example, fine-tuning, model size)
Subdomain 2.3: Build chat interfaces to interact with data in Snowflake.
• Set up the Snowflake environment • Required privileges • Invoke Cortex functions within the application code (for example, Streamlit) • Chat conversations • Multi-turn architecture • Update parameters
Subdomain 2.4: Use Snowflake Cortex functions in data pipelines.
• Snowflake Cortex • SQL interface • Extracting data from text using COMPLETE • Transcripts • 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 the 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 • CORTEX_MODELS_ ALLOWLIST parameter • Cortex LLM REST API • COMPLETE (SNOWFLAKE. CORTEX) • TRY_COMPLETE (SNOWFLAKE. CORTEX) • Cortex LLM Playground (Public Preview) • Data safety and security considerations • Is data leaving/going to LLMs? • REST API authentication methods
Subdomain 3.2: Set guardrails to filter out harmful or unsafe LLM responses.
• Cortex Guard • COMPLETE arguments • Methods to reduce model hallucinations and bias • Error conditions
Subdomain 3.3: Monitor and optimize Snowflake Cortex costs.
• Cortex Search • Different types of costs (virtual warehouse, EMBED_TEXT , Serving) • Cortex Analyst • Snowflake Service Consumption Table • Cortex LLM functions • Minimize tokens • Token cost implications • Tracking model usage and consumption • Usage quotas • CORTEX_FUNCTIONS_USAGE_HISTORY view • CORTEX_FUNCTIONS_ QUERY_USAGE_HISTORY view
Subdomain 3.4: Use Snowflake AI observability tools.
• Snowflake AI observability (Public Preview) features • Evaluation metrics • Comparisons • Tracing • Logging • Event tables • Implementation methods • Trulens SDK
Techniques & products