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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. An ML engineer is developing a RAG application in Python and wants to use the TruLens SDK to trace the distinct phases of its execution, specifically the context retrieval and answer generation steps. They aim to clearly differentiate the tracing of the function responsible for retrieving context.

A)

B)

C)

D)

E)

2. A security audit is being conducted for a financial institution using Snowflake Cortex. Which of the following statements accurately describe Snowflake's data safety and security guarantees concerning whether customer data, metadata, or prompts leave Snowflake's governance boundary to a third-party when using Cortex features, under the default Snowflake configurations for Cortex functions unless otherwise specified?

A) Customer Data and inputs to Snowflake AI Features are never used by Snowflake to train or fine-tune models made available to other customers.
B) When using SNOWFLAKE .CORTEX. COMPLETE with Snowflake-hosted LLMs like all prompts and generated responses remain within Snowflake's mistral-large2, governance boundary by default.
C) Models brought into Snowflake via Snowpark Container Services (BYOM) are treated as Snowflake's proprietary models, meaning Snowflake assumes responsibility for their data handling policies.
D) For Cortex Analyst, if the legacy ENABLE_CORTEX_ANALYST_MODEL_AZURE_OPENAI account parameter is set to TRUE, customer metadata and prompts are transmitted to Azure OpenAI, but the underlying customer data is not.
E) When CORTEX_ENABLED_CROSS_REGION is active for Cortex LLM functions, user inputs and outputs are always cached in the intermediate region to reduce latency, thereby leaving the primary region's immediate governance.

3. A developer is building an interactive chat application in Snowflake leveraging the COMPLETE (SNOWFLAKE. CORTEX) LLM function to power multi-turn conversations. To ensure the LLM maintains conversational context and generates coherent responses based on prior interactions, which of the following methods correctly implements the passing of conversation history to the COMPLETE function?

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

4. A business analyst is using a Cortex Analyst-powered conversational application to query structured data in Snowflake. They initially ask, 'What was the total profit from California last quarter?' and then follow up with, 'What about New York?' The application successfully provides accurate answers to both questions. Which of the following statements explain how Cortex Analyst supports this multi-turn conversational experience and maintains accuracy? (Select all that apply)

A) For multi-turn conversations, Cortex Analyst primarily relies on semantic search over sample values defined in the semantic model to infer context and generate SQL, making explicit conversation history management unnecessary.
B) Cortex Analyst stores the full, verbatim history of all previous user prompts and LLM responses, which are then passed to every subsequent LLM call to ensure complete context retention without any summarization.
C) The semantic model YAML file, which defines logical tables, dimensions, and measures, is crucial for Cortex Analyst to bridge the gap between business terminology and underlying technical schema, thereby improving text-to-SQL conversion accuracy for both initial and follow-up queries.
D) To handle follow-up questions, Cortex Analyst leverages an internal LLM summarization agent (e.g., Llama 3.1 70B) to reframe the current-turn question by retrieving context from the conversation history, rather than simply passing the entire history.
E) The accuracy of the SQL queries generated by Cortex Analyst for follow-up questions is significantly enhanced by its integration with a Verified Query Repository (VQR), which stores pre-verified natural language questions and their corresponding SQL queries.

5. An enterprise is deploying a Cortex Analyst application and needs to manage its cost, ensure data security, and understand its operational behavior within Snowflake. Which of the following statements are true regarding the deployment, cost, and security of Cortex Analyst and its semantic models?

A) Administrators can monitor Cortex Analyst requests, including the user, question asked, generated SQL, and errors, by querying the SNOWFLAKLOCAL .CORTEX_ANALYST_REQUESTS function.
B) The primary cost incurred for Cortex Analyst is based on the number of tokens processed by the underlying LLMs, with more complex natural language questions directly leading to higher token usage and charges.
C) Semantic models for Cortex Analyst, whether stored as YAML files or native semantic views, should have their access controlled by RBAC. This implicitly controls access to the underlying tables referenced in the semantic model.
D) When using Snowflake-hosted LLMs (e.g., from Mistral or Meta) with Cortex Analyst, all customer data, including metadata and prompts, remains within Snowflake's governance boundary.
E) Snowflake strongly recommends enabling the ENABLE_CORTEX_ANALYST_MODEL_AZURE_OPENAI account parameter to leverage Azure OpenAI models for Cortex Analyst, as it offers the highest performance and respects RBAC restrictions for these models.

Solutions: