Databricks Data Engineer Professional exam dumps

Databricks Data Engineer Professional practice question 271 of 313

Databricks Certified Data Engineer Professional. Professional level, Databricks. Free question with the correct answer and a full explanation.

Databricks Data Engineer Professional Question 271

Single answer

A data engineering team is using Databricks workflows to orchestrate a series of jobs that process and transform data. One of the jobs in the workflow failed due to a temporary issue when writing data to an external database. The team needs to repair the workflow and rerun only the failed task without re-executing the successful tasks. Which action should they take?

  1. A

    Use the 'Repair and Rerun' option in the workflow UI, ensuring only the failed task is selected for re-execution.

  2. B

    Manually delete the output of the failed task and restart the entire workflow from the beginning.

  3. C

    Clone the workflow, disable the successful tasks manually, and run the workflow again.

  4. D

    Modify the workflow’s settings to automatically retry failed tasks and wait for the next scheduled run.

Show answer and explanation

Correct answer: A

Explanation

Databricks workflows provide a 'Repair and Rerun' option to efficiently address failed tasks in a workflow. This feature allows users to rerun only the failed tasks without re-executing tasks that have already succeeded, saving time and resources. The other options involve inefficient or error-prone methods that do not leverage the built-in workflow management features of Databricks.

  • A. Correct.

    This is the correct approach as Databricks provides a 'Repair and Rerun' option in the workflow UI that allows you to rerun only the failed tasks without re-executing successful ones.

  • B. Incorrect.

    This is not efficient because restarting the entire workflow would re-execute all tasks, including those that succeeded, which wastes resources and time.

  • C. Incorrect.

    Cloning and manually disabling tasks is unnecessary and error-prone when Databricks provides a built-in mechanism for repairing and rerunning failed tasks.

  • D. Incorrect.

    While automatic retries are useful, they occur during the initial run of a task. This option does not address repairing and rerunning a failed task in a workflow after the initial run has completed.

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