A data platform team manages a Delta table that is queried frequently by a dashboard. The table receives small incremental updates every 15 minutes. Over time, the dashboard queries have become slower, and the team suspects that the table has too many small files. They want to improve query performance without affecting the freshness of the data. What should they do?

Run VACUUM on the table to delete old files.

Enable change data feed on the table.

Run OPTIMIZE on the table during off-peak hours.

Increase the number of partitions in the table.

A data pipeline runs a Lakeflow job every hour. The job occasionally fails due to transient cluster startup issues. The operations team wants to be notified only when the job fails, not when it succeeds. They already stream Databricks logs to a Log Analytics workspace. What should you configure in Azure Monitor?

An activity log alert that triggers when the Databricks workspace is deleted.

A metric alert rule that monitors the number of active cluster nodes and alerts when it drops to zero.

A log alert rule that queries the Databricks logs for job run events with a result state of 'Failed'.

A scheduled query that runs every hour and sends an email only if the job succeeded.

A data engineer is troubleshooting a Spark job that is running slower than expected. The Spark UI shows that a stage has a high shuffle write time, but the shuffle read time is low. The DAG shows that the stage is followed by a stage that performs an aggregation. What is the most likely cause of the high shuffle write time?

The mapper tasks are serializing and writing a large amount of data to disk.

The reducer tasks are reading a large amount of data from disk.

The aggregation is causing data skew.

The cluster has insufficient memory, causing spilling.

A Delta table is updated frequently with new records and occasional updates to existing records. Over time, queries that read the table have become slower. The table's directory contains many small Parquet files and a large number of Delta transaction logs. Which command should you run first to improve query performance?

Run ANALYZE TABLE to update statistics for the query optimizer.

Set spark.sql.files.maxPartitionBytes to a higher value to read larger chunks per task.

VACUUM the table to remove old files and reduce the number of files that queries must scan.

OPTIMIZE the table to compact small files into larger ones.

Microsoft Certified:Azure Databricks Data Engineer Associate

Domain 4Objective 4

Monitor, Troubleshoot, and Optimize Workloads in Azure Databricks DP-750 Practice Questions (Page 2)

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 6–10

6application · medium
A data platform team manages a Delta table that is queried frequently by a dashboard. The table receives small incremental updates every 15 minutes. Over time, the dashboard queries have become slower, and the team suspects that the table has too many small files. They want to improve query performance without affecting the freshness of the data. What should they do?
Select an answer first
7application · medium
A data pipeline runs a Lakeflow job every hour. The job occasionally fails due to transient cluster startup issues. The operations team wants to be notified only when the job fails, not when it succeeds. They already stream Databricks logs to a Log Analytics workspace. What should you configure in Azure Monitor?
Select an answer first
8expert · hard
A data engineer is troubleshooting a Spark job that is running slower than expected. The Spark UI shows that a stage has a high shuffle write time, but the shuffle read time is low. The DAG shows that the stage is followed by a stage that performs an aggregation. What is the most likely cause of the high shuffle write time?
Select an answer first
9application · medium
A Delta table is updated frequently with new records and occasional updates to existing records. Over time, queries that read the table have become slower. The table's directory contains many small Parquet files and a large number of Delta transaction logs. Which command should you run first to improve query performance?
Select an answer first
10foundation · easy
Which cluster management action is most appropriate when a cluster becomes unresponsive due to a node failure or network issue?
Select an answer first

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Monitor, Troubleshoot, and Optimize Workloads in Azure Databricks DP-750 Practice Questions (Page 2)

Questions 6–10

Which cluster management action is most appropriate when a cluster becomes unresponsive due to a node failure or network issue?

Keep studying — Deploy and maintain data pipelines and workloads