SnowPro Associate: Platform Question 27
Single answer● Snowflake NotebooksA data analyst is using Snowflake Notebooks to explore a large sales dataset and share findings with teammates. The analyst wants the notebook to run Python code directly against Snowflake-managed compute, access data from tables in the analyst's current database and schema, and allow teammates to rerun the notebook with their own role-based permissions. Which approach best meets these requirements?
- A
Create and run the notebook in Snowsight, using a warehouse for SQL cells and a Snowpark-compatible compute runtime for Python cells so queries and code execute within Snowflake under the current user's role.
- B
Export the notebook to a local Jupyter environment and connect with SnowSQL so Python cells inherit the notebook owner's privileges while SQL cells use the viewer's role.
- C
Store the notebook as a stage file and execute all cells through an external IDE, because Snowflake Notebooks cannot run Python code inside Snowflake.
- D
Run the notebook only with ACCOUNTADMIN, because Snowflake Notebooks require a single shared administrative role for all viewers who rerun notebook cells.
Show answer and explanation
Correct answer: A
Explanation
The best answer is the option that keeps development and execution inside Snowflake Notebooks in Snowsight. Snowflake Notebooks support interactive SQL and Python workflows for data exploration and analysis. In practical use, SQL execution relies on a selected virtual warehouse, while Python execution is supported through Snowflake-managed notebook compute integrated with Snowflake data access patterns such as Snowpark. This makes notebooks suitable for collaborative analysis without moving data to external tools. A key security and governance point for the SnowPro Associate level is that access to tables and other objects is controlled by Snowflake's role-based access control model; collaborators rerunning notebook cells do so in their own user context with their own active role and privileges. This is why using an administrative role for everyone is unnecessary and contrary to best practice. Relevant Snowflake documentation includes Snowflake Notebooks, Snowsight, Snowpark, virtual warehouses, and access control / RBAC guidance.
- A. Correct.
Correct. Snowflake Notebooks in Snowsight are designed to let users work interactively with SQL and Python in Snowflake. SQL cells use a selected warehouse, and Python cells can run using Snowflake-managed notebook compute that supports Snowpark-style data access. When teammates open and rerun the notebook, execution occurs in their own session and under their own active role and object privileges, which aligns with role-based access control requirements.
- B. Incorrect.
Incorrect. Exporting to a local Jupyter environment changes the execution model and does not satisfy the requirement to run directly on Snowflake-managed compute inside Snowflake Notebooks. Also, Snowflake does not use a model where notebook Python cells automatically inherit the notebook owner's privileges for other users; access is governed by each user's own role and grants unless a separate feature such as owner-rights procedures is explicitly involved.
- C. Incorrect.
Incorrect. This reflects a common misconception. Snowflake Notebooks are specifically intended to support interactive development in Snowflake, including Python execution integrated with Snowflake data access. While staged files and external IDEs can be used in some workflows, they are not required here and do not best meet the stated sharing and in-platform execution needs.
- D. Incorrect.
Incorrect. Snowflake Notebooks do not require ACCOUNTADMIN for normal use or rerun by collaborators. Following least-privilege best practices, users should work with the minimum role needed. Teammates rerunning a notebook use their own permissions, not a mandatory shared administrative role.