Databricks Data Engineer Associate Question 50
Select 2You are working on a Databricks workspace and notice that your cluster is experiencing performance degradation. After reviewing the cluster event logs, you observe repeated 'Out of Memory' errors on worker nodes. Additionally, a long-running job has failed multiple times due to resource allocation issues. Which of the following scenarios would justify restarting the cluster?
- A
To clear memory leaks or cache that may be consuming excessive resources
- B
To apply updated cluster libraries after installing them
- C
To increase the cluster's instance types for better performance
- D
To reset the cluster's logs that have reached their maximum retention period
- E
To restart a specific job that failed due to a temporary issue
Show answer and explanation
Correct answers: A, B
Explanation
Restarting the cluster is useful in scenarios where clearing memory leaks or applying updates (e.g., new libraries) is necessary. These actions require a fresh cluster environment to resolve resource-related issues or to implement library updates. Other scenarios, such as changing instance types or handling job-specific failures, require different actions and do not justify restarting the cluster.
- A. Correct.
Restarting the cluster can help clear memory leaks or cached data that might be causing resource exhaustion. This is a valid reason to restart.
- B. Correct.
Restarting the cluster is necessary to apply changes after updating or installing new libraries, as these changes do not take effect until the cluster restarts.
- C. Incorrect.
Changing the instance type requires recreating the cluster, not simply restarting it. Restarting alone cannot modify instance types.
- D. Incorrect.
Cluster log retention is managed by Databricks and does not require a cluster restart to address. Restarting the cluster does not affect log retention settings.
- E. Incorrect.
Restarting a cluster does not restart specific jobs. Jobs can be retried directly without restarting the cluster.