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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A new Gen AI specialist is setting up Document AI. They have successfully created the necessary database, schema, and a custom role named 'doc_ai specialist_role'. This custom role has been granted the 'SNOWFLAKE.DOCUMENT INTELLIGENCE CREATOR database role. However, when the specialist attempts to create a new Document AI model build in Snowsight, they receive the error: Unable to create a build on the specified database and schem a. Please check the documentation to learn more.
What is the most likely missing privilege for the that is preventing the model build creation?

A) The virtual warehouse assigned to the Document AI environment is not large enough to support model build creation.
B)

C)

D)

E)

2. A multi-national corporation uses Snowflake across several AWS regions. Their primary operational Snowflake account is in AWS US East (Ohio), but they need to leverage a specific AI_COMPLETE model, llama4-maverick, which is natively available in AWS US East 1 (N. Virginia) but not in US East (Ohio). To address this, the Snowflake administrator enables cross-region inference for their US East (Ohio) account.

A) Cross-region inference is fully supported for AI_COMPLETE in U.S. SnowGov regions for both inbound and outbound inference requests, provided the target model is available in the respective SnowGov region.
B) The query latency for cross-region inference with AI_COMPLETE is consistently low and predictable, as Snowflake's architecture is designed to completely negate the impact of geographical distance and network variations.
C) The llama4-maverick model is listed as natively available in AWS US East 1 (N. Virginia) and is supported for cross-region inference (AWS US Cross-Region), validating it as a suitable target for inference from US East (Ohio).
D) To enable cross-region inference for the US East (Ohio) account, the administrator would execute the command: ALTER ACCOUNT SET AWS_US' ; to allow inference requests to be processed in any AWS US region where the model is available. CORTEX_ENABLED_CROSS REGION =AWS_US' ;
E) User inputs, service-generated prompts, and the generated outputs from cross-region AI_COMPLETE calls are automatically stored or cached in the remote processing region to optimize performance for subsequent identical requests.

3. A data engineering team wants to deploy a proprietary PyCaret classification model, saved as pycaret_best_model.pkl, for inference within Snowpark Container Services (SPCS). They need to register this custom model in the Snowflake Model Registry. Which of the following is a correct and essential step in this process?

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

4. A data engineer is constructing a Retrieval Augmented Generation (RAG) pipeline in Snowflake to allow users to query a large corpus of unstructured customer support transcripts using natural language. The goal is to retrieve relevant transcript snippets and then use a Large Language Model (LLM) to generate an answer. Which sequence of steps and Snowflake components would effectively implement this RAG pipeline?

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

5. 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) The
B) Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
C) 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.
D) 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.
E) 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

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