To explore data interactively with Apache Spark in Microsoft Fabric, what should you create?

• A. Spark job definition
• B. Data Factory pipeline
• C. Notebook
• D. Spark script

Answer: (C)

Interactive exploration with Apache Spark in Fabric is best done with a notebook. Notebooks give you an interactive canvas where you can write Spark code in cells, run small pieces of work, and see results immediately. This immediate feedback is perfect for exploratory data analysis, trying different transformations, and iterating on questions about the data. You can mix code with explanations and create visualizations right next to the computations, which helps you build intuition and document steps as you go. In Fabric, you can run PySpark or Spark SQL inside a notebook on a Spark pool, connect to data sources, and progressively refine your queries.

In contrast, a Spark job definition is geared toward batch execution and production-style runs, not ad-hoc interrogation. A Data Factory pipeline orchestrates workflows and data movement, not interactive analysis. A Spark script is a static file you run, which lacks the step-by-step interactivity and immediate feedback that notebooks provide for exploration.