SnowPro Associate: Platform Question 61
Single answer1.4 Work with Snowflake Notebooks.A data analyst is using a Snowflake Notebook to explore sales data and build a repeatable analysis for the finance team. The analyst needs to run SQL queries, use Python for data manipulation in the same notebook, and ensure the notebook executes using Snowflake-managed compute rather than a local desktop environment. Which approach best meets these requirements?
- A
Create and run the notebook in Snowsight, attaching it to a warehouse for SQL cells and using the notebook's Python support for code cells within Snowflake.
- B
Export the notebook to a local Jupyter environment and connect to Snowflake from the analyst's laptop so both SQL and Python run together.
- C
Use only SQL worksheets in Snowsight because Snowflake Notebooks do not support Python code execution.
- D
Store Python scripts in an internal stage and call them directly from a worksheet, because notebooks cannot combine SQL and Python in one interface.
Show answer and explanation
Correct answer: A
Explanation
The best answer is Option 1 because Snowflake Notebooks are built for interactive, repeatable analysis directly in Snowsight, allowing users to combine SQL and Python in one environment. This is particularly useful for analytics and exploratory workflows where a user wants to query data, transform it programmatically, and document the process in a single artifact. From a SnowPro Associate perspective, the key applied concept is understanding that Snowflake Notebooks provide an integrated development and analysis experience within Snowflake, rather than requiring a separate local notebook tool. This aligns with Snowflake documentation describing Notebooks as a first-class feature in Snowsight for interactive development using Snowflake-managed resources and supported languages.
- A. Correct.
Correct. Snowflake Notebooks are designed to let users work interactively in Snowsight with multiple cell types, including SQL and Python, in a single notebook experience. The notebook uses Snowflake resources for execution, which aligns with the requirement to avoid relying on the analyst's local machine for compute. Attaching the notebook to a warehouse supports SQL execution, and Python code can also be run in the notebook environment.
- B. Incorrect.
Incorrect. While using a local Jupyter environment connected to Snowflake is possible in some workflows, it does not meet the stated requirement to use Snowflake-managed notebook execution instead of the analyst's local desktop environment. This option shifts execution responsibility to the laptop rather than keeping the work fully inside Snowflake Notebooks.
- C. Incorrect.
Incorrect. This reflects a common misconception. Snowflake Notebooks are specifically intended to support more than just SQL-based worksheet behavior. They allow interactive analysis with supported languages such as Python in addition to SQL, making them more suitable than a SQL-only worksheet for this scenario.
- D. Incorrect.
Incorrect. Staging Python scripts for use elsewhere is not the best fit for an interactive notebook requirement. The scenario explicitly calls for using SQL and Python together in the same notebook interface. Snowflake Notebooks provide that integrated experience directly, so this workaround is unnecessary and does not best satisfy the business need.