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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A development team is creating a new search application using Snowflake Cortex Search. They are currently using a 'snowflake-arctic- embed-I-v2.0' embedding model. After an initial load of 10 million rows, each with approximately 500 tokens of text, they observe a significant 'EMBED_TEXT_TOKENS' cost. They want to minimize these costs for future updates and ongoing operations. Considering their goal to optimize 'EMBED_TEXT_TOKENS' costs, which two strategies should the team prioritize for their Cortex Search Service?

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

2. An organization is planning to implement a new Retrieval Augmented Generation (RAG) application and has chosen Snowflake Cortex Search as its core retrieval engine. To effectively manage their budget, the finance and data teams need a clear understanding of the various cost components associated with deploying and operating a Cortex Search Service. Which of the following represent distinct cost categories directly attributable to the deployment and ongoing operation of a Snowflake Cortex Search Service?

A) Compute costs for LLM inference (e.g., SNOWFLAKE.CORTEX.COMPLETE) when the RAG application uses the retrieved context to generate responses.
B) Cloud Services compute for monitoring underlying base objects for changes to trigger search service refreshes.
C) Virtual warehouse compute used for refreshing the search service's index and processing base object changes.
D) ' Services compute specifically for generating vector embeddings of text data during the indexing and update processes.
E) Storage for the materialized source data and the optimized search index data structures within the Snowflake account.

3. An AI developer is testing a new RAG application in Snowflake. The application uses

in this scenario?

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

4. A development team is implementing a suite of generative AI applications on Snowflake, utilizing both SQL functions and the Cortex REST API. They prioritize content safety and plan to integrate Cortex Guard wherever possible. Considering the various interfaces for interacting with Snowflake Cortex LLMs, which of the following interfaces and functions support the direct use of Cortex Guard via the guardrails' argument or equivalent configuration?

A) The 'SNOWFLAKE.CORTEX.CLASSIFY_TEXT SQL function for text classification tasks.
B) The Snowflake Cortex LLM REST API when invoking the '/api/v2/cortex/inference:complete' endpoint.
C) The 'SNOWFLAKE.CORTEX.COMPLETE SQL function for generative AI tasks.
D) The 'SNOWFLAKCORTEX.TRY_COMPLETE SQL function, which is the error-tolerant version of 'COMPLETE.
E) The 'Cortex Playground' (Public Preview) when testing prompts and model settings.

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) 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

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

Solutions: