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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineering team is setting up a Retrieval Augmented Generation (RAG) application using Snowflake Cortex Search to provide contextual answers from customer support transcripts. The transcripts are stored in a Snowflake table named SUPPORT_TRANSCRIPTS. Which of the following statements are crucial considerations or accurate facts regarding the initial setup and configuration of the Cortex Search Service for this use case?

A) Columns specified in the ATTRIBUTES field during service creation are only used for filtering search results and do not need to be present in the source query.
B) Cortex Search is designed to get users up and running quickly with a hybrid (vector and keyword) search engine on text data, handling embedding, infrastructure maintenance, and search quality parameter tuning automatically.
C) The CREATE CORTEX SEARCH SERVICE command requires that CHANGE_TRACKING = TRUE be enabled on the source table, especially if the role creating the service is not the table owner. This ensures that the service can track updates to the base data.
D) Cortex Search Services currently support replication and cloning, allowing for easy disaster recovery and geographical distribution of the search index.
E) Snowflake recommends using a dedicated virtual warehouse of any size, including X-Large or 2X-Large, for each Cortex Search Service to ensure the fastest possible materialization of search indexes during creation and refresh.

2. An analytics team needs to use various Snowflake Cortex LLM functions and wants to understand the cost implications and governance mechanisms for controlling model access and usage. The "ACCOUNTADMIN' has restricted general LLM access using the 'CORTEX_MODELS_ALLOWLIST' parameter. The team is particularly interested in how model complexity and specific function calls impact billing and how access can be managed effectively for different user groups.

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

3. An ML engineer is preparing a Docker image for a custom LLM application that will be deployed to Snowpark Container Services (SPCS). The application uses a mix of packages, some commonly found in the Snowflake Anaconda channel and others from general open-source repositories like PyPI. They have the following Docker-file snippet and need to ensure the dependencies are correctly installed for the SPCS environment to support a GPU workload. Which of the following approaches for installing Python packages in the Dockerfile would ensure a robust and compatible setup for a custom LLM running in Snowpark Container Services, based on best practices for managing dependencies in this environment?

A)

B)

C)

D)

E)

4. An organization enforces strict LLM access control. The has configured 'CORTEX MODELS_ALLOWLIST ='mistral-large2" and executed 'CALL SNOWFLAKE.MODELS.CORTEX BASE MODELS REFRESH()'. A developer, whose role DEV ROLE has been granted 'SNOWFLAKE.CORTEX USER' and 'SNOWFLAKE."CORTEX-MODEL-ROLE-CLAUDE-3-5- SONNET"' , attempts to make a REST API call to 'api/v2/cortex/inference:complete' using 'claude-3-5-sonnet' as the model name in the request body. Which of the following statements are true regarding this scenario?

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

5. A data engineering team is preparing a large corpus of unstructured text documents for a Retrieval Augmented Generation (RAG) application in Snowflake, leveraging Cortex Search and LLM functions. They plan to use SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER as part of their data ingestion pipeline. What is the primary benefit of employing this helper function in the context of their RAG workflow?

A) It divides lengthy documents into smaller, manageable text chunks, which improves the precision of information retrieval and the relevance of downstream LLM responses.
B) It performs sentiment analysis on each chunk, allowing the RAG system to filter out negative or irrelevant content before retrieval.
C) It automatically translates documents into a target language, ensuring multilingual compatibility for the LLM.
D) It generates vector embeddings for each document chunk, eliminating the need for separate embedding models.
E) It compresses the text data to reduce storage costs in Snowflake stages before processing by embedding models.

Solutions: