Databricks Data Engineer Associate Question 48
Select 2You are working on a Databricks cluster and notice that several jobs are failing due to memory allocation errors. Additionally, after applying a new cluster configuration with upgraded instance types, the performance of the cluster has not improved as expected. In which scenario would restarting the cluster be useful?
- A
To apply the new cluster configuration changes and refresh the cluster state
- B
To automatically resolve syntax errors in the job code that are causing job failures
- C
To reset the cluster's Spark UI and logs for improved debugging
- D
To clear the cluster's cache and free up memory that might be contributing to the job failures
Show answer and explanation
Correct answers: A, D
Explanation
Restarting a Databricks cluster is useful when configuration changes (such as upgrading instance types or auto-scaling settings) are made but not yet applied, or when memory-related issues (e.g., memory leaks or excessive caching) are causing job failures. Restarting ensures the cluster operates with a clean state and updated configuration.
- A. Correct.
Restarting the cluster applies any new configuration changes (e.g., upgraded instance types) and ensures the cluster is using the updated resources.
- B. Incorrect.
Restarting the cluster does not resolve syntax errors in the job code; these errors must be fixed in the code itself.
- C. Incorrect.
Restarting the cluster does not reset or improve debugging for the Spark UI; Spark UI logs are independent of cluster state.
- D. Correct.
Restarting the cluster clears the memory and cache, which can help resolve memory allocation issues and improve job performance.