SnowPro Specialty: Gen AI Question 1
Single answer1.1 Define Snowflake's Gen AI principles, features, and best practices.A financial services company wants to deploy an internal Gen AI assistant in Snowflake to help analysts summarize policy documents and answer questions about approved internal knowledge sources. The security team requires that sensitive data remain governed in Snowflake, that access controls continue to apply to the source data, and that the solution follow Snowflake-recommended Gen AI best practices for accuracy and safety. Which approach should the architect recommend?
- A
Export the policy documents to an external LLM provider, generate embeddings and prompts outside Snowflake, and return the responses back into Snowflake for reporting.
- B
Use Snowflake Cortex capabilities within Snowflake, keep the documents and prompts governed by Snowflake roles and policies, and implement retrieval grounded on trusted enterprise data to reduce hallucinations.
- C
Fine-tune a public model on all policy documents by copying unrestricted data extracts to a developer-managed environment, because model customization is the primary Snowflake best practice for accuracy.
- D
Allow analysts to prompt a general-purpose model directly with unrestricted access to all database schemas, because broader model context is more important than enforcing existing Snowflake governance controls.
Show answer and explanation
Correct answer: B
Explanation
The best answer is Option 2 because it applies Snowflake's Gen AI principles in a realistic enterprise setting: keep data protected in Snowflake, preserve governance and access controls, and improve model output by grounding responses in approved internal data. Snowflake emphasizes secure AI on governed data, using existing Snowflake security controls and minimizing unnecessary data movement. For enterprise assistants, a retrieval-based pattern over trusted content is a widely recommended best practice to improve factuality and reduce hallucinations. This is generally preferable to exporting data to external platforms or granting overly broad access. Candidates should recognize that Snowflake Gen AI solutions are designed to combine AI capabilities with Snowflake's data governance, security, and enterprise data platform strengths.
- A. Incorrect.
Incorrect. This approach moves sensitive content outside Snowflake and weakens the governance posture the company explicitly wants to preserve. A core Snowflake Gen AI principle is to bring AI to governed data where possible, rather than exporting data unnecessarily to external systems. This option also makes it harder to consistently enforce Snowflake access controls, masking, and other policies on the data used for prompting and retrieval.
- B. Correct.
Correct. This approach aligns with Snowflake Gen AI principles and best practices: keep data in Snowflake, apply existing governance and role-based access controls, and use grounded retrieval on trusted enterprise content to improve response quality and reduce hallucinations. For enterprise use cases, grounding LLM responses with relevant internal context is a key best practice, especially when accuracy and compliance matter.
- C. Incorrect.
Incorrect. This reflects a common misconception that fine-tuning is the default or primary best practice for enterprise Gen AI accuracy. In many business scenarios, retrieval-based grounding on current enterprise data is more appropriate than copying data to another environment for customization. Copying unrestricted extracts to a developer-managed environment also conflicts with the requirement to keep sensitive data governed in Snowflake.
- D. Incorrect.
Incorrect. This violates least-privilege and governance best practices. Snowflake's Gen AI approach emphasizes secure, governed access to enterprise data, not broad unrestricted exposure. Giving a model access to all schemas increases the risk of overexposure of sensitive information and does not address answer quality as effectively as retrieval over approved sources.