Snowflake GES-C01

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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A company is building a chatbot for internal support, powered by Snowflake Cortex LLMs. The primary goals are to provide answers that are accurate, grounded in proprietary documentation, and to minimize factual 'hallucinations'. They are considering various strategies to achieve this. Which of the following statements correctly describe effective methods or tools within Snowflake for addressing these concerns?

A) Using Cortex Search as a Retrieval Augmented Generation (RAG) engine can enhance LLM responses by providing relevant context from proprietary documentation, thereby reducing hallucinations.
B) For tasks requiring LLMs to generate SQL queries from natural language, using the Cortex Analyst verified Query Repository (VQR) can improve accuracy by leveraging pre-verified SQL queries for similar questions.
C) AI Observability can be leveraged to systematically evaluate applications, measuring metrics like 'factual correctness and 'groundedness' to detect and mitigate hallucinations, especially in summarization.
D) Deploying a custom fine-tuned model using SNOWFLAKE.CORTEX.FINETUNE on proprietary documentation is the most effective approach to ensure factual accuracy for any LLM task.
E) Enabling Cortex Guard with guardrails: true directly addresses model hallucinations by ensuring responses are always factually correct and aligned with the provided context.

2. A machine learning engineering team is evaluating two different configurations of a Retrieval Augmented Generation (RAG) application. uses for generation, while uses 'mistral-7b' with a refined prompt for the same task. They aim to compare the and 'groundedness' of the generated responses, as well as the efficiency of context retrieval. Which of the following steps are crucial for setting up AI Observability in Snowflake to facilitate a meaningful side-by-side comparison and assess these specific metrics?

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

3. A Gen AI Specialist in Snowflake Cortex aims to fine-tune an LLM for enhanced task-specific performance. When creating a fine-tuning job using SNOWFLAKE. CORTEX. FINETUNE( 'CREATE', ... ) , which statement accurately describes the required training data format and a supported base model, aligning with Snowflake's Gen AI principles for leveraging LLMs?

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

4. 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) 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

E) 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.

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

Solutions: