A data engineer needs to monitor cluster usage to identify underutilized clusters that can be downsized or stopped. Which Azure Databricks feature provides metrics such as CPU utilization, memory usage, and executor count for this purpose?

Event log in the cluster configuration

Cluster Metrics tab in the cluster details page

A Spark notebook cell throws an error indicating that a column does not exist in a DataFrame. Which step should the engineer take first to troubleshoot the issue?

Increase the number of executors

Inspect the DataFrame schema using the .printSchema() method

A team runs a long-running Spark streaming job on an interactive cluster. The cluster is configured with autoscaling, but the team notices that the cluster does not scale down during periods of low input data. They want to reduce costs while maintaining the streaming job. What should they do?

Stop the cluster when input data is low and start it when data increases.

Increase the autoscaling maximum to allow more workers during peak times.

Restart the cluster to apply the new autoscaling settings.

Set the autoscaling minimum to a lower value.

Microsoft Certified:Azure Databricks Data Engineer Associate

Domain 4Objective 4

Monitor, Troubleshoot, and Optimize Workloads in Azure Databricks DP-750 Practice Questions (Page 8)

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 36–40

36foundation · easy
A data engineer needs to monitor cluster usage to identify underutilized clusters that can be downsized or stopped. Which Azure Databricks feature provides metrics such as CPU utilization, memory usage, and executor count for this purpose?
Select an answer first
37foundation · easy
Which Azure service is used to collect and analyze logs from Azure Databricks clusters, such as driver logs and job logs, in a centralized workspace?
Select an answer first
38foundation · easy
A Spark notebook cell throws an error indicating that a column does not exist in a DataFrame. Which step should the engineer take first to troubleshoot the issue?
Select an answer first
39foundation · easy
A Spark job is experiencing frequent garbage collection pauses and out-of-memory errors. Which configuration parameter should be increased to give each executor more memory for caching and processing?
Select an answer first
40application · medium
A team runs a long-running Spark streaming job on an interactive cluster. The cluster is configured with autoscaling, but the team notices that the cluster does not scale down during periods of low input data. They want to reduce costs while maintaining the streaming job. 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 8)

Questions 36–40

A data engineer needs to monitor cluster usage to identify underutilized clusters that can be downsized or stopped. Which Azure Databricks feature provides metrics such as CPU utilization, memory usage, and executor count for this purpose?

Which Azure service is used to collect and analyze logs from Azure Databricks clusters, such as driver logs and job logs, in a centralized workspace?

A Spark notebook cell throws an error indicating that a column does not exist in a DataFrame. Which step should the engineer take first to troubleshoot the issue?

A Spark job is experiencing frequent garbage collection pauses and out-of-memory errors. Which configuration parameter should be increased to give each executor more memory for caching and processing?

A team runs a long-running Spark streaming job on an interactive cluster. The cluster is configured with autoscaling, but the team notices that the cluster does not scale down during periods of low input data. They want to reduce costs while maintaining the streaming job. What should they do?

Keep studying — Deploy and maintain data pipelines and workloads