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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data analytics team is building a Retrieval Augmented Generation (RAG) application to provide contextual answers from a vast repository of internal documents stored in Snowflake. They are evaluating different strategies for generating and retrieving text embeddings to optimize the overall RAG pipeline's performance and relevance. Which of the following statements accurately describe performance considerations related to embedding generation and retrieval in this RAG context? (Select all that apply)

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

2. A data engineering manager needs to audit Cortex LLM function costs to identify specific SQL queries that are unexpectedly high in token consumption for the 'llama3.1-8b' model. They require granular analysis of prompt, completion, and guardrail token usage for these queries. Which of the following Snowflake methods or views would provide the necessary insights?

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

3. An ML engineer is deploying a custom PyTorch-based image classification model, obtained from Hugging Face, to Snowpark Container Services (SPCS). The deployment requires GPU acceleration on a compute pool named 'my_gpu_pool' and specific Python packages ('torch' , 'transformers' , 'opencv-python'). The scenario dictates that 'opencv-python' is only available via PyPl, while 'torch' and 'transformers' can be sourced from either conda-forge or PyPl. The engineer uses the Snowflake Model Registry to log the model. Which of the following and configurations correctly specify the necessary Python dependencies and GPU utilization for this inference service, adhering to Snowflake's recommendations?

A)

B)

C)

D)

E)

4. 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) 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.
B) 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.
C) 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.
D) 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.
E) Cortex Search Services currently support replication and cloning, allowing for easy disaster recovery and geographical distribution of the search index.

5. A Gen AI engineer is tasked with selecting the most suitable Large Language Model (LLM) from Snowflake Cortex AI for a new customer service chatbot. They need to rapidly prototype and compare different LLMs with varying parameters on a sample dataset before committing to a production deployment. Which of the following statements accurately describe how the Cortex Playground (Public Preview) can assist in this scenario?

A) It allows direct fine-tuning of selected LLMs with custom datasets within the playground interface to improve model performance for specific tasks.
B) It enables side-by-side comparison of model outputs for different LLMs and model settings, facilitating an informed decision on model selection.
C) It supports exporting the tested prompts and model configurations as Python code, ready for integration into a Snowpark ML pipeline.
D) It provides a mechanism to deploy the chosen LLM directly into Snowpark Container Services (SPCS) compute pools from within the playground for immediate production use.
E) It allows connection to a Snowflake table with textual data, processing up to 100 rows, to experiment with prompts directly on actual data.

Solutions: