You are creating a cloud flow that will use two Update Row actions to interact with Microsoft Dataverse. Neither of these actions are dependent on…

The proposed solution of creating two sequential 'Update a row' actions does not meet the goal of minimizing processing time. Sequential actions run one after the other, so the total execution time is the sum of the time taken for both actions. Since the two actions are not dependent on each other, they can be run concurrently. By placing the actions in parallel branches, they will execute simultaneously. The total processing time would then be determined by the longer of the two actions, which is inherently faster than or equal to running them sequentially.

A. Yes: This is incorrect. Sequential execution is not the most time-efficient method for independent tasks. Parallel execution is the standard practice for minimizing processing time in such scenarios.

1. Microsoft Learn. (2023). Run actions in parallel in cloud flows.

Section: "Run actions in parallel"

Content: "By default, actions in your cloud flows run sequentially, which means an action runs only after the previous action in the workflow finishes. This behavior works for many scenarios... However, when you want to run independent actions at the same time, you can create a parallel branch. A parallel branch lets you run multiple actions or branches simultaneously in your flow." This document explicitly contrasts sequential execution with the more efficient parallel execution for independent actions.

2. Microsoft Learn. (2023). Best practices for creating flows with Microsoft Dataverse.

Section: "Performance considerations"

Content: This document discusses optimizing flow performance. While it may not explicitly mention parallel branches, the principle of reducing wait times and total duration aligns with using parallel execution for non-dependent steps. The concept is to "reduce the number of actions" in a linear path, which parallelizing effectively does by shortening the critical path duration.