SnowPro Associate: Platform Question 63
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 monthly business reviews. The analyst wants the notebook cells to run SQL directly against Snowflake tables and also use Python for charts, without moving data out of Snowflake. Which approach best meets this requirement?
- A
Create and use the Snowflake Notebook with a warehouse for compute so the notebook can run SQL and Python in Snowflake
- B
Export the sales data from Snowflake to a local Jupyter environment, then run SQL and Python there for better notebook compatibility
- C
Use a Streamlit app instead of a notebook, because Snowflake Notebooks do not support mixing SQL and Python in the same analysis workflow
- D
Create an external function that calls a Python service, because Snowflake Notebooks can only execute SQL statements
Show answer and explanation
Correct answer: A
Explanation
Snowflake Notebooks are intended for interactive, Snowflake-native development and analysis. They allow users to combine SQL and Python in one environment while keeping data governed inside Snowflake. In practice, users attach or use Snowflake compute resources such as a warehouse to execute notebook operations. For SnowPro Associate, the key takeaway is that Snowflake Notebooks support common analytical workflows directly in Snowflake, reducing the need to export data to external notebook tools. This aligns with Snowflake best practices around minimizing unnecessary data movement and using Snowflake-native capabilities for analysis.
- A. Correct.
Correct. Snowflake Notebooks are designed for interactive analysis within Snowflake and support working with both SQL and Python in the same notebook experience. A warehouse provides the compute needed to run queries and notebook workloads. This approach keeps data in Snowflake rather than exporting it elsewhere, which aligns with the scenario requirement.
- B. Incorrect.
Incorrect. Exporting data to a local Jupyter environment moves data out of Snowflake, which the scenario explicitly wants to avoid. Although Jupyter can support SQL and Python, this is not the best Snowflake-native solution and adds governance, security, and data movement concerns.
- C. Incorrect.
Incorrect. Streamlit in Snowflake is useful for building data applications and dashboards, but it is not the best answer for an analyst who wants an interactive notebook workflow for exploration and repeatable analysis. The statement that Snowflake Notebooks do not support mixing SQL and Python is false.
- D. Incorrect.
Incorrect. External functions are for calling remote services from SQL, not for replacing the core notebook capability. The claim that Snowflake Notebooks can only execute SQL is incorrect, since notebooks support Python as well.