Databricks Data Engineer Associate Question 46
Single answerYou are working on a Databricks cluster that processes daily ETL jobs with Apache Spark. Recently, you notice that the cluster is failing to execute jobs consistently, and execution times have increased significantly. Logs indicate that the cluster's driver memory usage is close to its limit, and some executors are unresponsive. What action should you take to resolve the issue?
- A
Restart the cluster to clear any residual states and free up memory resources.
- B
Upgrade the Databricks runtime to the latest version to address potential software bugs.
- C
Repartition the input data to reduce the number of partitions processed by the cluster.
- D
Scale up the cluster by adding more nodes to handle the increased workload.
Show answer and explanation
Correct answer: A
Explanation
Restarting the cluster is an effective way to clear residual states and free up memory resources when the driver memory usage is near its limit and executors become unresponsive. Other options, while useful in different scenarios, do not directly address the immediate problem described.
- A. Correct.
Restarting the cluster clears the driver and executor memory, resets any residual states, and can resolve unresponsive behavior caused by memory saturation. This is the most appropriate action when the issue is related to memory limits.
- B. Incorrect.
Upgrading the Databricks runtime might be helpful in some cases but is not directly relevant to resolving immediate memory-related issues or unresponsive executors.
- C. Incorrect.
Repartitioning the data could optimize job performance but does not directly address the memory saturation or unresponsive executors in the cluster.
- D. Incorrect.
Scaling up the cluster can help handle increased workloads, but in this case, the problem is related to memory saturation and unresponsive executors, which restarting the cluster can resolve efficiently.