Partial runs are useful for which of the following?

Partial runs are useful for which of the following?

• A. Using incremental development to test each section of your workspace as it is built.
• B. Avoiding slow workspace development waiting to read in source data on every run.
• C. Feature caching mode automatically detects changes in schema for you and issues a warning.
• D. You can save caches with a template to share with colleagues while troubleshooting or validating workflows.

Answer: (A)

Partial runs focus on validating a workspace piece by piece. By running only a portion of the workspace—up to a specific transformer or within a defined subgraph—you can verify how each section processes data, map outputs, and catch issues early. This creates an accelerated feedback loop during development, letting you test each section as you build it without waiting for the entire dataset to flow through the full workspace. While caching or schema-detection features address other concerns, partial runs are specifically about testing incremental parts of the workflow as you iterate.

Partial runs focus on validating a workspace piece by piece. By running only a portion of the workspace—up to a specific transformer or within a defined subgraph—you can verify how each section processes data, map outputs, and catch issues early. This creates an accelerated feedback loop during development, letting you test each section as you build it without waiting for the entire dataset to flow through the full workspace. While caching or schema-detection features address other concerns, partial runs are specifically about testing incremental parts of the workflow as you iterate.