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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data application developer is tasked with building a multi-turn conversational AI application using Streamlit in Snowflake (SiS) that leverages the COMPLETE (SNOWFLAKE. CORTEX) LLM function. To ensure the conversation flows naturally and the LLM maintains context from previous interactions, which of the following is the most appropriate method for handling and passing the conversation history?

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

2. An ML Engineer has successfully deployed a custom text embedding model, 'my_embedder model', to a Snowpark Container Service named 'text embedding_service' within their Snowflake account. This model has an 'encode' method that accepts a string and returns a vector. They now need to integrate inference calls from this deployed model into various applications. Which of the following are valid ways to invoke this model for inference?

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

3. An enterprise is deploying a new RAG application using Snowflake Cortex Search on a large dataset of customer support tickets. The operations team is concerned about managing compute costs and ensuring efficient index refreshes for the Cortex Search Service, which needs to be updated hourly. Which of the following considerations and configurations are relevant for optimizing cost and performance of the Cortex Search Service in this scenario?

A) The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GB/month) having a minimal impact on overall billing.
B) For embedding text, selecting a model like

C) CHANGE_TRACKING
D) The
E) For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.

4. A business intelligence team wants to enable non-technical users to query structured data in Snowflake using natural language. They are considering Cortex Analyst. What is the primary role of a semantic model in Cortex Analyst to achieve this goal for structured/text-to-SQL use cases?

A) The semantic model provides a mapping between business-friendly terms and the underlying technical database schema, enhancing the LLM's ability to generate accurate SQL from natural language questions.
B) The semantic model directly executes SQL queries provided by end-users, bypassing the need for an LLM to generate them.
C) It serves as a cache for frequently requested data, reducing latency for natural language queries by providing pre-computed results.
D) The semantic model acts as a vector store, storing embeddings of all data columns to enable semantic search for natural language queries.
E) It stores user authentication credentials and data access policies, ensuring that only authorized users can interact with the data.

5. A compliance officer is reviewing the usage of Snowflake Cortex LLM functions and the Cortex REST API within their organization, specifically focusing on the implementation and impact of Cortex Guard. They observe several instances where 'guardrails' were enabled. Which of the following statements accurately describe the behavior and cost considerations of Cortex Guard when integrated with Snowflake Cortex LLM functions or the Cortex REST API?

A) When Cortex Guard is enabled and a response is deemed unsafe, the LLM-generated output is replaced with a predefined message, and only the input tokens for Cortex Guard processing ('guard_tokens') are billed, not the potentially unsafe completion tokens.
B) The underlying model for Cortex Guard is Meta's Llama Guard 3, and its processing costs are separate from the primary LLM inference costs.
C) Cortex Guard can be configured with a custom message using the argument in the options object for both 'COMPLETE SQL function and the Cortex REST API.
D) Cortex Guard operates by evaluating responses after the LLM has fully generated its content, and it incurs additional compute cost for both input and output tokens during its processing.
E) Cortex Guard is inherently part of all Cortex LLM functions and does not require explicit enablement via 'guardrails: TRUE for SQL functions or the REST API.

Solutions: