A Spark job is running slower than expected. In the Spark UI, the 'Executors' tab shows that one executor has a much higher 'Shuffle Read' and 'Task Time' than the others. The job performs a groupBy on a column with a few dominant values. What should you do to improve the job's performance?

Increase the number of executors to provide more parallelism.

Enable spark.sql.adaptive.coalescePartitions.enabled to reduce the number of tasks.

Use a two-phase aggregation: first aggregate by a combination of the key and a random salt, then aggregate the results.

Set spark.sql.shuffle.partitions to a higher value to create more tasks.

A team runs a Spark job that processes a variable workload. The job runs every hour, but the input size varies from 10 GB to 100 GB. The cluster is currently configured with a fixed number of 8 workers. The team wants to reduce costs while maintaining performance. What should they do?

Set the cluster to a fixed size of 16 workers to handle the maximum workload.

Enable autoscaling on the cluster with a minimum of 2 and maximum of 16 workers.

Use a job cluster that starts and stops for each run, with a fixed size of 8 workers.

Keep the cluster at 8 workers and use spot instances to reduce cost.

Microsoft Certified:Azure Databricks Data Engineer Associate

Domain 4Objective 4

Monitor, Troubleshoot, and Optimize Workloads in Azure Databricks DP-750 Practice Questions (Page 7)

Part of the Deploy and maintain data pipelines and workloads domain, which accounts for 30–35% of the DP-750 exam. Microsoft does not publish an official question count, but from its 120-minute exam (~50–80 total, ~15–28 in this domain), expect 4–7 from this objective — we provide 47 practice questions to prepare you well beyond it. (estimate)

47questions here

10free pages

11concepts

30–35%of the exam

Questions 31–35

31foundation · easy
Which Delta Lake command permanently deletes old files that are no longer referenced by the table's transaction log, helping to reduce storage costs?
Select an answer first
32application · medium
A Spark job is running slower than expected. In the Spark UI, the 'Executors' tab shows that one executor has a much higher 'Shuffle Read' and 'Task Time' than the others. The job performs a groupBy on a column with a few dominant values. What should you do to improve the job's performance?
Select an answer first
33foundation · easy
Which Azure Databricks feature automatically adjusts the number of workers in a cluster based on the workload, helping to balance performance and cost?
Select an answer first
34foundation · easy
To implement log streaming from Azure Databricks to Log Analytics, which component must be installed on the cluster to forward logs?
Select an answer first
35application · medium
A team runs a Spark job that processes a variable workload. The job runs every hour, but the input size varies from 10 GB to 100 GB. The cluster is currently configured with a fixed number of 8 workers. The team wants to reduce costs while maintaining performance. What should they do?
Select an answer first

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Monitor, Troubleshoot, and Optimize Workloads in Azure Databricks DP-750 Practice Questions (Page 7)

Questions 31–35

Which Delta Lake command permanently deletes old files that are no longer referenced by the table's transaction log, helping to reduce storage costs?

A Spark job is running slower than expected. In the Spark UI, the 'Executors' tab shows that one executor has a much higher 'Shuffle Read' and 'Task Time' than the others. The job performs a groupBy on a column with a few dominant values. What should you do to improve the job's performance?

Which Azure Databricks feature automatically adjusts the number of workers in a cluster based on the workload, helping to balance performance and cost?

To implement log streaming from Azure Databricks to Log Analytics, which component must be installed on the cluster to forward logs?

A team runs a Spark job that processes a variable workload. The job runs every hour, but the input size varies from 10 GB to 100 GB. The cluster is currently configured with a fixed number of 8 workers. The team wants to reduce costs while maintaining performance. What should they do?

Keep studying — Deploy and maintain data pipelines and workloads