nerdexam
Microsoft

PL-500 · Question #61

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might…

The correct answer is B. No. Azure Machine Learning is an external Azure service that adds integration overhead to a Power Automate cloud flow, which does not satisfy the requirement to minimize processing time.

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Question

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. 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 each other. You must minimize the amount of processing time require to complete the flow. You need to implement the actions in the cloud flow. Solution: Use Azure machine learning to create the model. Does the solution meet the goal?

Options

  • AYes
  • BNo

How the community answered

(55 responses)
  • A
    5% (3)
  • B
    95% (52)

Why each option

Azure Machine Learning is an external Azure service that adds integration overhead to a Power Automate cloud flow, which does not satisfy the requirement to minimize processing time.

AYes

Selecting Yes is incorrect because Azure Machine Learning is not natively integrated into Power Automate or Dataverse, and its use would add external service overhead that conflicts with the goal of minimizing processing time.

BNoCorrect

Azure Machine Learning operates outside the Power Platform ecosystem and requires external HTTP connector calls or custom connectors to interact with Power Automate and Dataverse. This external dependency introduces additional network latency, authentication handshakes, and configuration complexity, all of which increase rather than minimize the total processing time of the flow.

Concept tested: External vs native service integration overhead in Power Automate

Source: https://learn.microsoft.com/en-us/azure/machine-learning/overview-what-is-azure-machine-learning

Topics

#Power Automate Cloud Flows#Dataverse Integration#Performance Optimization#Azure Machine Learning

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