Which features can you utilize within Foundry's debugger panel while debugging a Python transform? Select three.

Utilizing the debugger panel in Palantir Foundry while debugging a Python transform includes several important functionalities that support data engineers in identifying and resolving issues in their code efficiently.

One feature is the ability to preview intermediate dataframes at breakpoints. This allows users to inspect the state of their data at various points in the execution flow, making it easier to understand how data transformations are functioning and whether they are producing the expected results. Having visibility into intermediate outputs is crucial for diagnosing problems and ensuring that each step of the transformation is working as intended.

Running PySpark commands in the console is another capability found in the debugger panel. This feature enables users to execute commands using PySpark, which is essential for manipulating large datasets within a distributed framework. By running PySpark commands directly within the debugger, users can interactively test segments of their code, validate data functionality, and get immediate feedback on their operations.

Editing the source code directly from the debugger is yet another aspect that enhances the debugging process. This feature allows data engineers to modify their code in real-time as they identify issues, without needing to switch back to a separate environment or editor. The ability to make changes on the fly simplifies the debugging workflow, enabling quicker iterations and fixes.

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