A data engineer is troubleshooting a slow Spark job. In the Spark UI, they see that a stage has a high number of shuffle read records but a low number of shuffle read bytes. They also notice that the DAG shows a wide dependency. What is the most likely cause of the slowdown?

A large number of small files being read from the source.

Network congestion between executors.

Excessive disk spilling due to insufficient executor memory.

Data skew, where a few tasks process a disproportionate number of records.

A data engineer is analyzing a query profile for a Spark SQL query that runs slowly. The query profile shows that a sort operation is consuming a large amount of time and spilling to disk. The query sorts a large DataFrame by a column that has many duplicate values. What should the engineer do to improve performance?

Add a cache on the DataFrame before the sort.

Use a bucketed table for the column being sorted.

Add a filter to reduce the number of rows before the sort.

Increase the number of shuffle partitions.

A SQL analyst runs a query on a large Delta table. The query is slow, and the query profile shows that the 'Scan' operator reads 10 times more data than the final result set. The table has a column 'event_date' that is frequently used in the WHERE clause. What should you recommend?

Partition the table by event_date.

Use the OPTIMIZE command to compact small files.

Increase the number of executors to speed up the scan.

Create a Z-ORDER index on the event_date column.

In Azure Monitor, what is the primary purpose of a metric alert?

To visualize metrics in dashboards

To notify when a monitored metric crosses a defined threshold

To store log data for later analysis

To automatically scale clusters

Microsoft Certified:Azure Databricks Data Engineer Associate

Domain 4Objective 4

Monitor, Troubleshoot, and Optimize Workloads in Azure Databricks DP-750 Practice Questions (Page 5)

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 21–25

21application · medium
A data engineer is troubleshooting a slow Spark job. In the Spark UI, they see that a stage has a high number of shuffle read records but a low number of shuffle read bytes. They also notice that the DAG shows a wide dependency. What is the most likely cause of the slowdown?
Select an answer first
22application · medium
A data engineer is analyzing a query profile for a Spark SQL query that runs slowly. The query profile shows that a sort operation is consuming a large amount of time and spilling to disk. The query sorts a large DataFrame by a column that has many duplicate values. What should the engineer do to improve performance?
Select an answer first
23application · medium
A SQL analyst runs a query on a large Delta table. The query is slow, and the query profile shows that the 'Scan' operator reads 10 times more data than the final result set. The table has a column 'event_date' that is frequently used in the WHERE clause. What should you recommend?
Select an answer first
24foundation · easy
In Azure Monitor, what is the primary purpose of a metric alert?
Select an answer first
25foundation · easy
A data engineer needs to cancel a running Lakeflow Job that is consuming excessive resources. Which action should the engineer use?
Select an answer first

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Monitor, Troubleshoot, and Optimize Workloads in Azure Databricks DP-750 Practice Questions (Page 5)

Questions 21–25

A data engineer is troubleshooting a slow Spark job. In the Spark UI, they see that a stage has a high number of shuffle read records but a low number of shuffle read bytes. They also notice that the DAG shows a wide dependency. What is the most likely cause of the slowdown?

A SQL analyst runs a query on a large Delta table. The query is slow, and the query profile shows that the 'Scan' operator reads 10 times more data than the final result set. The table has a column 'event_date' that is frequently used in the WHERE clause. What should you recommend?

In Azure Monitor, what is the primary purpose of a metric alert?

A data engineer needs to cancel a running Lakeflow Job that is consuming excessive resources. Which action should the engineer use?

Keep studying — Deploy and maintain data pipelines and workloads