A job that processes a daily data feed failed because the input file path changed. The job uses a parameter `input_path` in the notebook. The engineer needs to rerun the job with the new path without editing the notebook. Which approach should the engineer use?

Create a new job with the same notebook but a different parameter value.

Use the 'Repair run' option and override the `input_path` parameter with the new path.

Edit the notebook to hardcode the new path and then run the job.

Update the job's cluster configuration to mount the new path.

A Spark job is running slowly, and the Spark UI shows a stage with high shuffle write but low shuffle read. The query profile shows a large number of partitions. Which is the most likely cause of the slowdown?

Network congestion between executors.

Insufficient executor memory causing disk spills.

Excessive partitioning leading to small tasks and high shuffle overhead.

Data skew causing some tasks to process much more data than others.

DatabricksCertified Data Engineer Professional

Domain 9Objective 1

Debugging and Troubleshooting DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 7)

Part of the Debugging and Deploying domain, which accounts for 10% of the DATA-ENGINEER-PROFESSIONAL exam. Databricks does not publish an official question count, but from its 120-minute exam (~50–80 total, ~5–8 in this domain), expect 3–4 from this objective — we provide 34 practice questions to prepare you well beyond it. (estimate)

34questions here

7free pages

9concepts

10%of the exam

Questions 31–34

31foundation · easy
A Spark job fails with an executor error. Where in the Databricks cluster logs would you look to find the detailed stack trace for the executor failure?
Select an answer first
32application · medium
A job that processes a daily data feed failed because the input file path changed. The job uses a parameter `input_path` in the notebook. The engineer needs to rerun the job with the new path without editing the notebook. Which approach should the engineer use?
Select an answer first
33expert · hard
A Spark job is running slowly, and the Spark UI shows a stage with high shuffle write but low shuffle read. The query profile shows a large number of partitions. Which is the most likely cause of the slowdown?
Select an answer first
34foundation · easy
In the Spark UI, which tab would you use to inspect the number of tasks that succeeded, failed, or were skipped for a particular stage?
Select an answer first

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Debugging and Troubleshooting DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 7)

Questions 31–34

A Spark job fails with an executor error. Where in the Databricks cluster logs would you look to find the detailed stack trace for the executor failure?

A job that processes a daily data feed failed because the input file path changed. The job uses a parameter `input_path` in the notebook. The engineer needs to rerun the job with the new path without editing the notebook. Which approach should the engineer use?

A Spark job is running slowly, and the Spark UI shows a stage with high shuffle write but low shuffle read. The query profile shows a large number of partitions. Which is the most likely cause of the slowdown?

In the Spark UI, which tab would you use to inspect the number of tasks that succeeded, failed, or were skipped for a particular stage?

Keep studying — Debugging and Deploying