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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A Gen AI specialist is designing a RAG pipeline utilizing Cortex Search for an application that queries a large repository of unstructured text documents. To optimize the quality of retrieval and subsequent LLM responses, what are the critical best practices and understanding of Cortex Search's mechanisms that the specialist should consider regarding text processing and tokenization?

A) When text input exceeds an embedding model's context window, Cortex Search truncates the text for both semantic embedding and keyword-based retrieval, potentially losing critical information.
B) Embedding models with larger context windows, such as snowflake-arctic-embed-1-v2.e-8k (8000 tokens), are always superior as they allow the RAG system to process the entire document as a single, highly relevant chunk.
C) Cortex Search operates solely on vector embeddings for semantic search; keyword-based retrieval is handled by a separate, less efficient mechanism outside the core search service.
D) The SNOWFLAKE .CORTEX. COUNT TOKENS function is a helper function that can be used to accurately determine the token count for a given string based on a specified model, aiding in adherence to context window limits.
E) For best search results, text in the search column should be split into chunks of no more than 512 tokens, as smaller chunks generally lead to more precise retrieval and relevant LLM context.

2. data scientist is designing a Retrieval Augmented Generation (RAG) system in Snowflake for querying a large knowledge base of internal documents. They plan to store document embeddings and use vector similarity for retrieval. Which statement accurately describes the use of vector functions and associated costs in this RAG architecture?

A) Snowpark Python fully supports all Snowflake vector similarity functions, including

B) When using

C)

D) The

E) Storing vector embeddings, created using functions like

3. A data science team is deploying a custom real-time inference service for a fine-tuned LLM using Snowpark Container Services (SPCS). They have a Docker image in their Snowflake image repository. They need to define the service using a YAML specification file. Which of the following are ''essential'' components or configurations that must be included in the 'spec.yaml' file for a long- running service that uses this image, custom environment variables, and requires external access?

A)

B)

C)

D)

E)

4. A Snowflake Gen AI Specialist is defining a semantic model for Cortex Analyst to improve text-to-SQL accuracy. They are adding entries to the verified _ queries section of their YAML file. Consider the following semantic model snippet and a proposed verified_query entry. Which of the following statements correctly identifies an issue or a best practice not followed in the sql field of the proposed verified_query entry, based on Cortex Analyst VQR guidelines? Semantic Model Snippet:

Proposed verified _ query entry:

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

5. A developer has successfully created a Cortex Search Service named within knowledge_base. Services based on a large repository of technical product manuals. They now need to query this service to find sections discussing 'troubleshooting network issues' specifically for products tagged as 'ModelX', returning only the 3 most relevant results and including the 'manual_title' and 'section_id' in the output. Which of the following SQL commands correctly performs this query?

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

Solutions: