Databricks Data Engineer Associate Question 454
Single answerYou are configuring a Databricks job to process a large dataset from a Delta Lake table. The job occasionally fails due to transient network issues when writing data back to the Delta table. Which approach should you use to implement a retry policy for the job in case of failure?
- A
Enable task retries in the job configuration and set the maximum retries to a non-zero value.
- B
Wrap the job logic in a try-except block within the notebook to manually handle retries.
- C
Use a custom Python library to implement retries within the job tasks.
- D
Configure the Databricks cluster to automatically retry failed jobs at the cluster level.
Show answer and explanation
Correct answer: A
Explanation
The correct way to implement a retry policy for a Databricks job is to enable task retries in the job configuration. This allows Databricks to automatically retry failed tasks up to a specified maximum number of retries, making it an effective and efficient solution for handling transient failures.
- A. Correct.
This is correct. Databricks provides a built-in option to configure task retries in the job configuration. This is the recommended way to handle transient failures in Databricks jobs.
- B. Incorrect.
This is incorrect. While a try-except block can handle failures, it does not integrate with Databricks' job retry mechanisms and requires manual implementation.
- C. Incorrect.
This is incorrect. Using a custom Python library to implement retries is unnecessary and not aligned with Databricks' built-in features for handling retries.
- D. Incorrect.
This is incorrect. Databricks does not provide cluster-level configurations for automatically retrying failed jobs. Retry policies need to be set at the job level.