To interactively explore data in a file in the lakehouse using Spark, which option should you use?

To interactively explore data in a file in the lakehouse using Spark, which option should you use?

• A. Switch to the SQL analytics endpoint mode
• B. Use a Spark shell
• C. Create a notebook
• D. Create a Dataflow (Gen2)

Answer: (C)

Notebooks provide an interactive workspace where you can run Spark code in small, runnable cells, inspect results as you go, and annotate your steps as you explore a file in the lakehouse. This setup supports an iterative workflow: load the data with Spark, apply transformations, preview outputs, refine queries, and visualize results all in one place. The other options serve different purposes—a Spark shell is a command-line REPL, which is great for quick ad-hoc experiments but isn’t as convenient for documenting steps or producing rich, repeatable explorations; switching to the SQL analytics endpoint mode emphasizes SQL queries through an endpoint rather than interactive Spark exploration; and a Dataflow (Gen2) is designed for building and orchestrating data pipelines, not for interactive data discovery.

Notebooks provide an interactive workspace where you can run Spark code in small, runnable cells, inspect results as you go, and annotate your steps as you explore a file in the lakehouse. This setup supports an iterative workflow: load the data with Spark, apply transformations, preview outputs, refine queries, and visualize results all in one place. The other options serve different purposes—a Spark shell is a command-line REPL, which is great for quick ad-hoc experiments but isn’t as convenient for documenting steps or producing rich, repeatable explorations; switching to the SQL analytics endpoint mode emphasizes SQL queries through an endpoint rather than interactive Spark exploration; and a Dataflow (Gen2) is designed for building and orchestrating data pipelines, not for interactive data discovery.