Databricks Data Engineer Associate exam dumps

Databricks Data Engineer Associate practice question 451 of 532

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

Databricks Data Engineer Associate Question 451

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You are running a Databricks job that processes a large dataset using multiple tasks in a job workflow. One of the tasks has failed, and you need to debug the issue. Upon inspecting the failed task, you notice the error message indicates an 'OutOfMemoryError' during a shuffle operation. What steps should you take to resolve this issue?

  1. A

    Increase the executor memory for the job.

  2. B

    Enable adaptive query execution (AQE) in the Spark configuration.

  3. C

    Inspect the task's logs to identify the stage where the shuffle operation failed.

  4. D

    Reduce the parallelism of the job to lower the number of tasks.

  5. E

    Consider using broadcast joins to reduce shuffle operations if applicable.

Show answer and explanation

Correct answers: A, B, C, E

Explanation

When a task fails due to an 'OutOfMemoryError' during a shuffle operation, the recommended steps include increasing executor memory, enabling AQE for dynamic partition optimization, and inspecting logs to pinpoint the failure stage. Additionally, using broadcast joins can reduce shuffle operations if the dataset allows for it. Reducing parallelism, however, is not a suitable solution as it might increase task memory requirements, making the problem worse.

  • A. Correct.

    Increasing the executor memory can help resolve 'OutOfMemoryError' issues by providing more resources for processing large shuffle operations.

  • B. Correct.

    Enabling adaptive query execution (AQE) allows Spark to optimize shuffle partitions dynamically, which can help prevent memory-related errors during shuffles.

  • C. Correct.

    Inspecting the task's logs is critical for understanding the root cause of the failure and identifying the stage where the issue occurred.

  • D. Incorrect.

    Reducing parallelism is not advisable in this case because it can increase the workload on individual tasks, potentially exacerbating memory issues.

  • E. Correct.

    Using broadcast joins can eliminate shuffle operations for certain types of joins, which may help reduce memory pressure during shuffles.

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