Partial runs and feature caching are used to test a portion of the workspace and speed up testing.

Partial runs and feature caching are used to test a portion of the workspace and speed up testing.

• A. To test the entire workspace
• B. To test a portion of the workspace and speed up testing
• C. To disable caching
• D. To increase data size

Answer: (B)

Partial runs and feature caching focus the testing effort and speed up iteration by letting you execute only a portion of the workspace and reuse results from previous processing. In FME, you can start the run at a specific point so only the upstream portion is executed, which lets you quickly validate logic around a subset of transformers without running the entire workflow. Feature caching stores the results of processed features so unchanged data can skip reprocessing through downstream transformers, dramatically cutting run time during development. Together, these techniques let you verify changes in a targeted area while keeping iterations fast. They aren’t about testing the entire workspace, they don’t disable caching, and they don’t increase data size.

Partial runs and feature caching focus the testing effort and speed up iteration by letting you execute only a portion of the workspace and reuse results from previous processing. In FME, you can start the run at a specific point so only the upstream portion is executed, which lets you quickly validate logic around a subset of transformers without running the entire workflow. Feature caching stores the results of processed features so unchanged data can skip reprocessing through downstream transformers, dramatically cutting run time during development. Together, these techniques let you verify changes in a targeted area while keeping iterations fast. They aren’t about testing the entire workspace, they don’t disable caching, and they don’t increase data size.