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Snowflake GES-C01 Exam Syllabus Topics:

Section	Objectives
Topic 1: Snowflake AI & Cortex	- AI functions and services in Snowflake - Snowflake Cortex capabilities
Topic 2: Prompt Engineering	- Optimization of prompts for LLM outputs - Prompt design techniques
Topic 3: Embeddings, Vector Search & RAG	- Embeddings fundamentals - Retrieval-Augmented Generation (RAG) workflows - Vector search in Snowflake ecosystem
Topic 4: Use Cases & Solution Design	- End-to-end GenAI solution architecture - Enterprise AI application patterns in Snowflake
Topic 5: Data Governance & Security	- Responsible use of AI in enterprise environments - Data privacy and access controls
Topic 6: Generative AI Fundamentals	- Model capabilities and limitations - Core concepts of generative AI and LLMs
Topic 7: Model Evaluation & Responsible AI	- Bias, fairness, and explainability considerations - Evaluation metrics for LLM outputs

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A new ML Engineer, 'data_scientist_role' , has been assigned to a project involving custom machine learning models in Snowflake. They need to gain the necessary permissions to perform the following actions related to Snowflake Model Registry and Snowpark Container Services: 1. Log a custom model into a specified schem a. 2. Deploy that model to an existing Snowpark Container Service compute pool. 3. Call the deployed model for inference using SQL. Which of the following SQL commands grant the 'minimal' required privileges to the for these actions, assuming the compute pool and image repository already exist and are appropriately configured?

A)

B)

C)

D)

E)

2. A Snowflake administrator needs to implement a granular access control strategy for LLMs. The general policy is to restrict access to a select few models via an account-level allowlist. However, a specific data science team (using role 'DATA SCIENCE TEAM ROLE) requires access to the 'claude-3-5-sonnet' model, which should not be available to other users or globally via the allowlist. Given this scenario, which set of commands would correctly establish this access control while adhering to the specified requirements?

A)

B)

C)

D)

E)

3. A data engineering team is optimizing an AI-infused pipeline that processes millions of rows of customer interaction data in a LOG_DATA table using various Snowflake Cortex AI functions. They need to accurately estimate costs and ensure optimal performance. Which of the following statements regarding cost, performance, and operational considerations for these functions are true?

A) Option B
B) Option A
C) Option E
D) Option D
E) Option C

4. A data scientist is tasked with improving the accuracy of an LLM-powered chatbot that answers user questions based on internal company documents stored in Snowflake. They decide to implement a Retrieval Augmented Generation (RAG) architecture using Snowflake Cortex Search. Which of the following statements correctly describe the features and considerations when leveraging Snowflake Cortex Search for this RAG application?

A) For optimal search results with Cortex Search, source text should be pre-split into chunks of no more than 512 tokens, even when using models with larger context windows like

B) To create a Cortex Search Service, one must explicitly specify an embedding model and manually manage its underlying infrastructure, similar to deploying a custom model via Snowpark Container Services.
C) The
D) Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
E) Enabling change tracking on the source table for the Cortex Search Service is optional; the service will still refresh automatically even if change tracking is disabled.

5. A company is implementing a Document AI solution to extract sensitive financial data from invoices. They plan to fine-tune the Document AI model (Arctic-TILT) and then manage this custom model within the Snowflake Model Registry. Which of the following statements correctly outlines the access control, data handling, and model management principles for this scenario?