Databricks Data Engineer Associate Question 456
Select 3You are designing a Databricks notebook to process a batch of data from an external API. Occasionally, the API fails due to rate limits or transient issues, and you need to implement a retry policy to ensure the pipeline processes the data successfully. Which of the following approaches are appropriate for setting up a retry policy in Databricks notebooks?
- A
Use a try-except block to catch the exceptions and retry the operation a fixed number of times.
- B
Leverage the Databricks Task Retry feature to retry failed tasks at the job level.
- C
Implement a loop with a sleep or delay mechanism to retry the operation after a certain time interval.
- D
Directly increase the cluster size to avoid transient errors caused by resource limitations.
- E
Use Databricks Delta to automatically retry the operation on failure.
Show answer and explanation
Correct answers: A, B, C
Explanation
Setting up a retry policy in Databricks requires implementing mechanisms to handle transient issues, such as using try-except blocks, leveraging the Databricks Task Retry feature, or implementing a loop with a delay. These techniques ensure that failures due to temporary issues, such as rate limits or network instability, are retried until successful or until a maximum retry limit is reached. Increasing cluster size or relying on Databricks Delta are not appropriate solutions for this scenario.
- A. Correct.
This is a valid approach. Using a try-except block allows you to catch exceptions and control the retry logic programmatically, including specifying the number of retry attempts.
- B. Correct.
This is correct. The Databricks Task Retry feature can be configured at the job level to automatically retry failed tasks, which is a robust way to handle transient errors.
- C. Correct.
This is a valid approach. Implementing a loop with a delay mechanism (e.g., using the time.sleep() function) is another way to handle temporary failures programmatically.
- D. Incorrect.
This is incorrect. Increasing the cluster size does not directly address transient failures caused by external API rate limits or network issues.
- E. Incorrect.
This is incorrect. Databricks Delta is primarily used for data storage and does not provide functionality to automatically retry API operations.