SnowPro Associate: Platform Question 62
Single answer1.4 Work with Snowflake Notebooks.A data analyst is using a Snowflake Notebook to explore quarterly sales data and build visualizations for a review meeting. The analyst wants notebook SQL and Python cells to run successfully against the same Snowflake data without moving data outside Snowflake, while also controlling compute cost during interactive development. Which approach best meets these requirements?
- A
Use the notebook with a warehouse for executing SQL and Python cells, querying Snowflake tables directly, and resize or suspend the warehouse when needed to manage cost.
- B
Export the sales data from Snowflake to a local Jupyter environment so Python can process it, then reconnect to Snowflake only for final reporting.
- C
Create the notebook without assigning any compute resource because notebooks store enough metadata to execute SQL and Python code on their own.
- D
Use Snowflake Notebooks only for documentation and charts, and run all SQL and Python transformations in an external IDE because notebooks cannot access Snowflake tables directly.
Show answer and explanation
Correct answer: A
Explanation
The best answer is to use Snowflake Notebooks as an in-platform analytical workspace that works directly with Snowflake data and uses Snowflake compute for execution. For SnowPro Associate-level understanding, the key ideas are: notebooks are used for interactive analysis; they can access Snowflake data directly; and compute management remains important for cost control. In practice, using an appropriately sized warehouse and suspending or resizing it when development pauses is a standard cost-management approach. Options that require exporting data or treating notebooks as non-executable documentation conflict with Snowflake best practices of minimizing data movement and keeping analytics close to governed data. Refer to Snowflake documentation on Snowflake Notebooks and virtual warehouses for execution and cost-management concepts.
- A. Correct.
Correct. Snowflake Notebooks are designed to work directly with data in Snowflake. Notebook cells can run against Snowflake using compute resources, and a warehouse is used for execution in common notebook workflows. This lets the analyst query Snowflake tables directly without exporting data, and warehouse sizing or suspension is an appropriate way to control interactive development cost.
- B. Incorrect.
Incorrect. Exporting data to a local Jupyter environment adds unnecessary data movement and undermines one of the main advantages of Snowflake Notebooks: working with Snowflake data directly in-platform. It also introduces governance, consistency, and security concerns that the scenario specifically tries to avoid.
- C. Incorrect.
Incorrect. A notebook is not a standalone execution engine. To run SQL or Python code, Snowflake requires compute resources. The misconception is that the notebook object itself provides execution capability, but execution depends on underlying Snowflake compute rather than notebook metadata alone.
- D. Incorrect.
Incorrect. Snowflake Notebooks are not limited to static documentation or visualization. They are intended for interactive data exploration and development with Snowflake data. This option reflects a common misunderstanding that notebooks in Snowflake are only presentation tools rather than executable analytical workspaces.