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Snowflake GES-C01 Exam Syllabus Topics:

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

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineering team is setting up an automated pipeline to extract information from new invoices using Document AI. They've created a database and schema Cinvoice_db.invoice_schema') and a Document AI model build They then created an internal stage for documents. When they attempt to run the method on documents uploaded to 'invoice_stage' , they consistently receive the following error:

Given this error message, which 'corrective SQL command' addresses the most likely misconfiguration of the 'invoice_stage' to allow Document AI processing?

A)

B)

C)

D)

E)

2. A developer is building a conversational application using Snowflake Cortex Analyst and is interacting with it via the REST API. The application needs to support multi-turn conversations where users can ask follow-up questions. Which of the following statements accurately describe how to maintain conversational state and interact with the Cortex Analyst REST API for a multi-turn experience?

A) The Cortex Analyst REST API automatically retains the full conversation history for each user session, eliminating the need to explicitly pass previous messages.
B) The argument, when using the 'COMPLETE function (or its API equivalent), must be a string for single-turn conversations but an array of objects for multi-turn conversations, with each object containing 'role' and 'content'.
C) For performance optimization, only the most recent user prompt and the immediate preceding analyst response should be passed in the 'messages' array for follow-up questions.
D) A special 'RESET_CONVERSATION' API endpoint should be called periodically to clear the LLM's internal context and manage token consumption for long conversations.
E) To maintain state, the application must construct a 'messages' array for each new prompt, including all prior 'user' prompts and 'analyst' responses in chronological order, and send it with every API call.

3. A data engineer is setting up a Document AI pipeline to extract information from scanned invoices stored in an internal stage named 'invoice_stage'. They have created the stage using 'CREATE STAGE and uploaded several PDF documents. However, when attempting to run the extraction query, they encounter an error message: 'File extension does not match actual mime type. Mime- Type: application/octet-stream'. Additionally, they anticipate a privilege issue might arise for pipeline automation. Which of the following conditions must be met to resolve the current error and ensure proper setup for Document AI extraction and subsequent pipeline creation?

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

4. A data application developer is using the Snowflake Cortex COMPLETE function to power a multi-turn conversational AI application. They want to ensure responses are creative but not excessively long, adhere to a specific JSON structure, and are filtered for safety. Given the following SQL query snippet, which statements accurately describe the impact of the specified options?

A) For a multi-turn conversation, previous user prompts and model responses should be passed in the

B) Including a
C) The

D) The

E) Setting

5. A data engineering team is tasked with improving the accuracy of a Cortex Analyst solution for a large e-commerce product catalog. Users frequently ask natural language questions involving specific product names, brands, and categories. The team observes that Cortex Analyst sometimes struggles to identify and correctly filter by these literal values in the generated SQL. Which of the following configurations or approaches, within the semantic model, can effectively enhance Cortex Analyst's ability to precisely identify and use literal values for filtering, based on Snowflake's best practices?