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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data architect is evaluating the shift from managing Cortex Analyst semantic models as YAML files on internal stages to leveraging a native semantic view (currently in Public Preview). They want to understand the key differences and advantages or considerations of this new native approach. Which of the following statements accurately describe a key characteristic or implication of using native semantic views for Cortex Analyst, compared to YAML files stored in a stage?

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

2. An enterprise is designing an advanced generative AI application in Snowflake, leveraging Cortex Agents to orchestrate data analysis from both structured and unstructured sources. According to Snowflake's Gen AI principles and the capabilities of Cortex Agents, which of the following statements accurately describe the workflow components and the types of tools an agent can utilize?

A) The agent's workflow includes 'Planning' to orchestrate a solution, 'Explore options' for disambiguation, and 'Reflection' to determine next steps after tool use. Supported tools include Cortex Analyst and Cortex Search.
B) For debugging, Cortex Agents allow direct modification of the LLM's internal state to refine accuracy, latency, and cost during execution.
C) Cortex Agents can orchestrate across both structured and unstructured data sources, and custom tools can be implemented using Snowflake stored procedures and user-defined functions (UDFs).
D) Cortex Agents are restricted to using only Snowflake's native Cortex LLM functions; custom logic via UDFs or stored procedures is not supported for tool implementation.
E) Cortex Agents primarily focus on pre-defined, single-turn SQL queries for structured data, with limited support for unstructured data processing.

3. A data engineer is tasked with implementing a product recommendation system in Snowflake. They have pre-computed product embeddings and want to find similar items using VECTOR_COSINE_SIMILARITY They are evaluating options for interacting with this function. Which of the following statements is TRUE regarding the use of VECTOR_COSINE_SIMILARITY and Snowflake's VECTOR data type?

A) The maximum dimension supported by the

B) The

C) A column defined as

D) Direct comparison operators like

E)

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

5. A data engineering team wants to deploy a proprietary PyCaret classification model, saved as pycaret_best_model.pkl, for inference within Snowpark Container Services (SPCS). They need to register this custom model in the Snowflake Model Registry. Which of the following is a correct and essential step in this process?