SnowPro Associate: Platform Question 26
Single answer● Snowflake NotebooksA data engineering team wants to use Snowflake Notebooks to explore sales data and share findings with analysts. The team lead wants a solution that keeps data inside Snowflake, allows Python-based analysis in notebook cells, and uses Snowflake-managed compute rather than requiring each user to connect an external IDE. Which approach best meets these requirements?
- A
Create and run a Snowflake Notebook in Snowsight, using Python cells backed by Snowflake compute and querying data stored in Snowflake tables
- B
Export the sales data from Snowflake to local CSV files and analyze it in Jupyter Notebook on each analyst's laptop
- C
Use an external BI dashboard only, because Snowflake Notebooks do not support Python-based analysis
- D
Develop a Streamlit app outside Snowflake, because Snowflake Notebooks require data to be moved to a client machine before analysis
Show answer and explanation
Correct answer: A
Explanation
The best answer is to use Snowflake Notebooks in Snowsight. Snowflake Notebooks provide an interactive environment for analysis and development within the Snowflake platform, helping teams keep data governed and centralized rather than exporting it to local environments. For SnowPro Associate-level understanding, the key applied concept is recognizing when Snowflake-native tools meet requirements for in-platform analytics, Python-based exploration, and Snowflake-managed execution. This is consistent with Snowflake guidance around using Snowsight-based experiences and keeping processing close to the data for security and operational simplicity.
- A. Correct.
Correct. Snowflake Notebooks are designed for interactive development and analysis directly in Snowflake. They support Python in notebook cells and are intended to keep data in the Snowflake environment instead of requiring export to local tools. This aligns with the scenario's goals of in-platform analysis, data remaining in Snowflake, and use of Snowflake-managed compute.
- B. Incorrect.
Incorrect. Exporting data to CSV and analyzing it locally breaks the requirement to keep data inside Snowflake and introduces governance, security, and version-control concerns. While analysts commonly use Jupyter locally, that approach is specifically what the team lead wants to avoid.
- C. Incorrect.
Incorrect. This reflects a misunderstanding of Snowflake Notebooks. Snowflake Notebooks are specifically intended to support notebook-style analysis workflows, including Python-based work. A BI dashboard may be useful for visualization, but it does not replace the notebook requirement in the scenario.
- D. Incorrect.
Incorrect. This option incorrectly assumes Snowflake Notebooks require data movement to a client machine. A key value of Snowflake Notebooks is working close to the data within Snowflake. Streamlit can be used for app development, but it is not the best answer to a requirement centered on interactive notebook analysis inside Snowflake.