SnowPro Associate: Platform Question 25
Single answer● Snowflake NotebooksA data analyst wants to use Snowflake Notebooks to explore a sales table, create visualizations, and share the analysis with teammates who also work in Snowflake. The analyst wants the notebook to run close to the data and avoid moving data to an external environment. Which approach best meets these requirements?
- A
Create and run the notebook in Snowflake, using a warehouse-backed runtime so code executes within Snowflake and can query Snowflake tables directly
- B
Export the sales table to a local Jupyter environment because Snowflake Notebooks only support viewing results, not running code against Snowflake data
- C
Convert the notebook into a stage file and execute it through SnowSQL, because Snowflake Notebooks cannot be shared with other Snowflake users
- D
Use a database role to store Python packages inside a schema, because Snowflake Notebooks require packages to be manually uploaded before any analysis can begin
Show answer and explanation
Correct answer: A
Explanation
The best answer is to use Snowflake Notebooks natively in Snowflake with compute provided by Snowflake, such as a warehouse-backed runtime, so analysis runs near the data. This aligns with Snowflake best practices of minimizing unnecessary data movement and using built-in platform capabilities for analysis and collaboration. Snowflake documentation describes Notebooks as a way to perform interactive data analysis and development directly inside Snowflake. For SnowPro Associate-level understanding, candidates should recognize that Snowflake Notebooks are intended to support in-platform exploration, coding, and sharing rather than requiring external notebook tools or manual workarounds.
- A. Correct.
Correct. Snowflake Notebooks are designed to let users develop and run notebook-based analysis directly in Snowflake. Using a warehouse-backed runtime allows the notebook to execute close to the data, query Snowflake tables directly, and support collaborative analytics without exporting data to an external platform.
- B. Incorrect.
Incorrect. This reflects a common misconception based on traditional notebook workflows. Snowflake Notebooks are not limited to static result viewing; they are intended for interactive development and analysis within Snowflake, reducing the need to export data to local environments.
- C. Incorrect.
Incorrect. While SnowSQL can run SQL scripts, Snowflake Notebooks are a native Snowflake feature and do not need to be converted into stage files for execution. The claim that notebooks cannot be shared with other Snowflake users is also incorrect in the context of Snowflake object access and collaboration within the platform.
- D. Incorrect.
Incorrect. Snowflake Notebooks do not require Python packages to be manually stored in a schema via a database role before any analysis can occur. This option confuses package management and object security concepts. Notebook environments in Snowflake are designed to support analysis workflows without that manual prerequisite.