A team wants to set up proactive monitoring for their Azure Databricks workloads. They have configured diagnostic settings to stream logs to Log Analytics. They want to receive an alert when a job fails more than three times in an hour. What should they do?

Use the Databricks CLI to check job status every minute and send an email.

Create a metric alert rule in Azure Monitor for job failures.

Configure a webhook in the Lakeflow job to call an endpoint after each failure.

Create a log alert rule in Azure Monitor with a query that counts job failures and a threshold of 3.

A company runs Azure Databricks workloads and wants to centralize cluster and job logs in Azure Monitor Log Analytics for troubleshooting. They have already configured the diagnostic settings to stream logs to a Log Analytics workspace. However, they notice that the logs are not appearing in the workspace. What should they check first?

Verify that the cluster has the 'Enable autoscaling' option enabled.

Ensure that the cluster is using the Premium tier.

Confirm that the diagnostic settings are configured at the Azure Databricks workspace level and that the selected log categories are enabled.

Check that the cluster has a public IP address.

A data engineering team runs a nightly Spark job that joins a large fact table with a small dimension table. The job completes but takes twice as long as expected. In the Spark UI, the DAG shows a single stage with a large number of tasks, and the 'Shuffle Read Size' metric is very high. The cluster has 16 workers with 8 cores each. Which action is most likely to reduce the job's runtime?

Increase the number of partitions for the shuffle by setting spark.sql.shuffle.partitions to 256.

Broadcast the small dimension table by using a broadcast hint or by increasing spark.sql.autoBroadcastJoinThreshold.

Increase the number of worker nodes to 32 to provide more memory for the shuffle.

Enable spark.sql.adaptive.shuffle.reducePostShufflePartitions.enabled to automatically reduce partitions after the shuffle.

Which Spark configuration parameter controls the number of partitions used when shuffling data during operations like joins and aggregations?

spark.sql.autoBroadcastJoinThreshold

Microsoft Certified:Azure Databricks Data Engineer Associate

Domain 4Objective 4

Monitor, Troubleshoot, and Optimize Workloads in Azure Databricks DP-750 Practice Questions (Page 3)

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 11–15

11expert · hard
A team wants to set up proactive monitoring for their Azure Databricks workloads. They have configured diagnostic settings to stream logs to Log Analytics. They want to receive an alert when a job fails more than three times in an hour. What should they do?
Select an answer first
12application · medium
A company runs Azure Databricks workloads and wants to centralize cluster and job logs in Azure Monitor Log Analytics for troubleshooting. They have already configured the diagnostic settings to stream logs to a Log Analytics workspace. However, they notice that the logs are not appearing in the workspace. What should they check first?
Select an answer first
13application · medium
A data engineering team runs a nightly Spark job that joins a large fact table with a small dimension table. The job completes but takes twice as long as expected. In the Spark UI, the DAG shows a single stage with a large number of tasks, and the 'Shuffle Read Size' metric is very high. The cluster has 16 workers with 8 cores each. Which action is most likely to reduce the job's runtime?
Select an answer first
14foundation · easy
A Lakeflow Job fails due to a transient error in one task. The engineer wants to rerun only the failed task without restarting the entire job. Which action should be taken?
Select an answer first
15foundation · easy
Which Spark configuration parameter controls the number of partitions used when shuffling data during operations like joins and aggregations?
Select an answer first

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Monitor, Troubleshoot, and Optimize Workloads in Azure Databricks DP-750 Practice Questions (Page 3)

Questions 11–15

A team wants to set up proactive monitoring for their Azure Databricks workloads. They have configured diagnostic settings to stream logs to Log Analytics. They want to receive an alert when a job fails more than three times in an hour. What should they do?

A Lakeflow Job fails due to a transient error in one task. The engineer wants to rerun only the failed task without restarting the entire job. Which action should be taken?

Which Spark configuration parameter controls the number of partitions used when shuffling data during operations like joins and aggregations?

Keep studying — Deploy and maintain data pipelines and workloads