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Snowflake GES-C01 Exam Syllabus Topics:

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

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. An AI engineer is building an automated pipeline in Snowflake that processes various types of textual data using Cortex AI functions. To ensure the pipeline's stability and avoid failures due to exceeding LLM context windows, they integrate SNOWFLAKE.CORTEX.COUNT_TOKENS and TRY_COMPLETE
. Consider the following code snippets and statements about context window management in Snowflake Cortex.

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

2.
Which 'combination of missing schema-level privileges' is explicitly cited in the documentation as a direct cause for this error, assuming a unique model build name?

A)

B)

C)

D)

E)

3. A data application developer is tasked with building a multi-turn conversational AI application using Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To ensure the conversation flows naturally and the LLM maintains context from previous interactions, which of the following is the most appropriate method for handling and passing the conversation history?

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

4. A Gen AI developer is implementing a Document AI solution to extract key fields from thousands of diverse PDF reports, which vary significantly in length and complexity. They use the '!PREDICT method with 'GET_PRESIGNED_URL' to process documents from an external stage. After initial testing, they observe two distinct types of errors in the query results:

for other, lengthy PDF files. Which two of the following actions should the developer take to resolve these issues?

A) Reconfigure the external stage to use
B) Redesign the input documents to ensure they do not exceed 125 pages per file, or preprocess by splitting overly long documents into multiple smaller files.
C) Increase the virtual warehouse size to a Large or X-Large to speed up processing and prevent URL expiration.
D) Grant the

E) Implement a mechanism to process documents in smaller batches or extend the expiration time for the presigned URLs to ensure timely access by Document

5. An enterprise is deploying a new RAG application using Snowflake Cortex Search on a large dataset of customer support tickets. The operations team is concerned about managing compute costs and ensuring efficient index refreshes for the Cortex Search Service, which needs to be updated hourly. Which of the following considerations and configurations are relevant for optimizing cost and performance of the Cortex Search Service in this scenario?