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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
B) 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.
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) The

E) 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

2. A Gen AI Specialist is tasked with implementing a data pipeline to automatically enrich new customer feedback entries with sentiment scores using Snowflake Cortex functions. The new feedback arrives in a staging table, and the enrichment process must be automated and cost-effective. Given the following pipeline components, which combination of steps is most appropriate for setting up this continuous data augmentation process?

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

3. A data analyst is setting up a new Cortex Analyst-powered conversational app for business users. They want to understand how the "Suggested Questions" feature behaves under different semantic model configurations to ensure an optimal user experience. Which of the following statements accurately describe the behavior of the "Suggested Questions" feature in Cortex Analyst based on the semantic model configuration?

A) If the semantic model does not include a Verified Query Repository (VQR), Cortex Analyst will always return a blank list of suggested questions.
B) In the absence of a VQR, Cortex Analyst uses underlying Large Language Models (LLMs) to generate up to three suggested questions, which are guaranteed to be answerable.
C) Setting the

D) If multiple verified queries in a VQR are marked with

E) When a semantic model contains a VQR, Cortex Analyst prioritizes returning up to five suggested questions from the VQR that are semantically similar to the user's input.

4. A data engineering team is tasked with creating vector embeddings for a collection of diverse, multilingual research papers for a semantic search application. They need to use 'SNOWFLAKE.CORTEX.EMBED TEXT 1024' and are considering two models: 'snowflake-arctic-embed-l-v2.0' and 'voyage-multilingual-2'. They also need to ensure the resulting embeddings are stored correctly and understand potential text truncation. Which of the following statements correctly describes the application of the 'EMBED TEXT 1024' function for these models and the characteristics of the generated embeddings?

A) The 'snowflake-arctic-embed-I-v2.0' model has a larger context window than 'voyage-multilingual-2 , making it more suitable for longer research papers without truncation.
B) Using either 'snowflake-arctic-embed-l-v2.0' or 'voyage-multilingual-2' with will result in a 'VECTOR(FLOAT, 1024)' data type, which is compatible with 'VARIANT columns.
C) The function call

D) Both 'snowflake-arctic-embed-l-v2.0' and 'nv-embed-qa-4' are multilingual models with a 1024-dimension output, making them interchangeable for this use case.
E) The query

5. A data engineer is working with Snowflake Cortex Analyst to improve its ability to answer natural language questions by precisely identifying product names for filtering. They have decided to integrate a Cortex Search Service with their semantic model to enhance literal search for the 'product_name' dimension. Which of the following configurations within the semantic model's YAML file are valid and effective for this purpose?

A) Setting true' for the 'product_name' dimension and providing an exhaustive list of to restrict the model to only those values.
B) Adding a 'cortex_search_service' entry to the 'product_name' dimension with only the 'service' field:

C) Including in the semantic model's 'metrics' section, referencing 'product_name'.
D) Adding a entry to the 'product_name' dimension, including 'literal_column' and ensuring the search service is configured to index the physical column:

E) Only specifying 'sample_valueS for the 'product_name' dimension without a entry.

Solutions: