SnowPro Specialty: Gen AI Question 2
Single answer1.1 Define Snowflake's Gen AI principles, features, and best practices.A financial services company wants to build an internal assistant in Snowflake that summarizes support tickets and answers employee questions using company knowledge. The security team requires that sensitive enterprise data remain governed inside Snowflake, and the platform team wants to minimize custom infrastructure while following Snowflake Gen AI best practices. Which approach BEST meets these requirements?
- A
Export ticket data daily to an external vector database and third-party LLM service, because Snowflake Gen AI features are intended only for unstructured files rather than governed enterprise tables.
- B
Use Snowflake Cortex AI functions and related Gen AI capabilities directly on data in Snowflake, applying role-based access controls and existing governance policies so prompts and retrieved context follow the same data protections as other workloads.
- C
Fine-tune a custom model outside Snowflake first, then reload the generated embeddings and responses into Snowflake, because governance can only be applied after AI outputs are written back to tables.
- D
Move all support ticket data into a separate Snowflake account dedicated to AI, because Snowflake best practice is to isolate Gen AI workloads from the source data used by business analysts.
Show answer and explanation
Correct answer: B
Explanation
The best answer is to use Snowflake's native Gen AI capabilities, such as Cortex AI functions, directly where the enterprise data already lives. A key Snowflake Gen AI principle is minimizing data movement and applying AI within the platform's existing security, governance, and access-control framework. This helps organizations use structured and unstructured data for AI while preserving centralized governance and reducing operational overhead. In practice, best practices include keeping sensitive data in Snowflake when possible, using existing RBAC and governance features, and preferring native platform capabilities over unnecessary external pipelines when requirements can be met internally. These principles are consistent with Snowflake documentation on Cortex AI, data governance, and secure AI application design.
- A. Incorrect.
Incorrect. This option conflicts with a core Snowflake Gen AI principle: bring AI to the data rather than moving governed enterprise data out unnecessarily. Exporting sensitive data to external services increases operational complexity and governance risk. Snowflake Gen AI capabilities are designed to work with structured and unstructured data in Snowflake, not just files.
- B. Correct.
Correct. This is the best fit for the scenario and aligns with Snowflake Gen AI principles and best practices: use built-in Cortex AI capabilities on governed data where it already resides, minimize data movement, and rely on Snowflake security and governance controls such as roles and policies. This reduces infrastructure overhead while supporting secure, practical enterprise AI adoption.
- C. Incorrect.
Incorrect. This reflects a common misconception that custom external model work is required before Snowflake can be used effectively for Gen AI. While organizations may choose external model workflows in some cases, it is not the best answer here because the stated goal is to keep data governed inside Snowflake and minimize custom infrastructure. Governance in Snowflake applies to the data and access model before and during AI workflows, not only after outputs are stored.
- D. Incorrect.
Incorrect. Creating a separate account solely for AI is not a general Snowflake Gen AI best practice. It can add complexity and may fragment governance rather than simplify it. The requirement is to keep sensitive data governed and reduce custom infrastructure, which is better addressed by using native Snowflake capabilities with existing controls instead of introducing unnecessary account separation.