What makes dynamic workflows dynamic?

What makes dynamic workflows dynamic?

• A. They dynamically define or construct the schema at run-time, not beforehand.
• B. They dynamically scale the number of FME Server engines being used to account for the volume of data being processed.
• C. They dynamically reproject data to the user's chosen coordinate system.
• D. They dynamically speed up and slow down data processing to save energy.

Answer: (A)

Dynamic workflows are designed to adapt to the data as it flows, building or defining the schema at run-time rather than locking it in ahead of time. This means the workspace can handle varying feature types and attribute sets on the fly, creating or adjusting the output structure based on what the input actually contains. That on-the-fly schema construction is what makes a workflow dynamic in this context. In practice, you might connect a reader that brings in different datasets with different attributes, and the workflow uses transformers configured to infer or generate the needed attributes at processing time, avoiding the need to predefine every possibility. The other options describe runtime changes that don’t involve adapting the data schema itself: scaling server engines is about resource management, reprojecting is a geometric transformation to a chosen coordinate system, and speeding up or slowing down processing for energy reasons is about performance, not dynamic schema construction.

Dynamic workflows are designed to adapt to the data as it flows, building or defining the schema at run-time rather than locking it in ahead of time. This means the workspace can handle varying feature types and attribute sets on the fly, creating or adjusting the output structure based on what the input actually contains. That on-the-fly schema construction is what makes a workflow dynamic in this context.

In practice, you might connect a reader that brings in different datasets with different attributes, and the workflow uses transformers configured to infer or generate the needed attributes at processing time, avoiding the need to predefine every possibility.

The other options describe runtime changes that don’t involve adapting the data schema itself: scaling server engines is about resource management, reprojecting is a geometric transformation to a chosen coordinate system, and speeding up or slowing down processing for energy reasons is about performance, not dynamic schema construction.