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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 - Snowflake Gen AI principles and best practices - Cortex AI components: Cortex Search, Cortex Analyst, Cortex LLMs - Role-based access control (RBAC) for AI resources |
| Snowflake Gen AI Governance | 22% | - Cost management and token-based pricing - Monitoring, logging, and observability - Audit and compliance for AI workloads - Guardrails, safety controls, and bias mitigation - AI governance framework and policies |
| Snowflake Gen AI & LLM Functions | 40% | - Embedding functions: EMBED_TEXT_*, vector storage and similarity search - Model deployment with Snowpark Container Services and Model Registry - Cortex LLM functions: COMPLETE, CLASSIFY_TEXT, EXTRACT_ANSWER, SENTIMENT, SUMMARIZE, TRANSLATE - API integration and authentication - RAG implementation in Snowflake |
| Snowflake Document AI | 12% | - Document preparation and processing - Performance optimization and troubleshooting - Document AI setup and configuration - Data extraction and structured output |
Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:
1. An organisation is deploying a Snowflake Cortex Agent to assist business users with data insights. To enable users to interact with this agent via the agent : run API, which of the following database roles or privileges must be granted to their account role?
A) The
B) The
C) CREATE EXTERNAL AGENT
D) The
E) The
2. A data scientist has fine-tuned a Hugging Face sentence transformer model for semantic search and intends to deploy it to Snowpark Container Services (SPCS) via the Snowflake Model Registry. The model requires GPU acceleration and specific Python packages ('sentence-transformerS, 'torch', 'transformers'). A GPU compute pool named 'my_gpu_pool' is available. Which of the following code snippets correctly logs the model and deploys it as a service to SPCS, ensuring it utilizes the GPU compute pool and has the necessary Python dependencies for the Hugging Face model and PyTorch?
A)
B)
C)
D)
E) 
3. A data engineering team is building an automated pipeline in Snowflake to process customer reviews. They need to use AI_COMPLETE to extract specific details like product, sentiment, and issue type, and store them in a strictly defined JSON format for seamless downstream integration. They aim to maximize the accuracy of the structured output and manage potential model limitations. Which statements accurately reflect the best practices and characteristics when using AI_COMPLETE with structured outputs for this scenario?
A) Option B
B) Option D
C) Option A
D) Option C
E) Option E
4. An organization relies on Snowflake Cortex LLM functions and has established a robust model governance policy using the 'CORTEX MODELS_ALLOWLIST parameter. A developer is integrating 'TRY COMPLETE into an application for processing various text inputs. Which of the following statements are correct regarding 'TRY COMPLETE and model access controls?
A) The parameter primarily governs access for models invoked by their plain names with but 'TRY COMPLETE can still access models if the user has been granted appropriate RBAC on a fully-qualified model object.
B) if a If a model specified in 'TRY_COMPLETE is not allowed by the and no RBAC grant exists for it, the 'TRY_COMPLETE function will automatically return 'NULL' to prevent a hard error.
C) The 'TRY_COMPLETE function can return a 'NULL ' value if the underlying LLM experiences a temporary internal processing error during response generation, allowing the query execution to continue without halting.
D) To provide a stateful conversational experience with 'TRY_COMPLETE, the function automatically retains context from previous calls within the same session.
E) If 'TRY_COMPLETE fails to perform its operation and returns ' NULL ' , the tokens that would have been processed for that specific call are not billed, ensuring cost efficiency for failed operations.
5. A Gen AI Specialist is tasked with enhancing a Cortex Analyst semantic model to improve the accuracy of literal string searches for product names within user queries. The product names are stored in a high-cardinality PRODUCT_NAME column in the underlying PRODUCT table. The current semantic model already defines a dimension for PRODUCT_NAME. Which of the following configurations and considerations are essential for integrating Cortex Search with Cortex Analyst to achieve this goal?
A) Option B
B) Option D
C) Option A
D) Option C
E) Option E
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B,E | Question # 3 Answer: A,D,E | Question # 4 Answer: A,C,E | Question # 5 Answer: A |
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