A data engineering team uses Lakeflow Spark Declarative Pipelines to process streaming data. They notice that a pipeline occasionally fails due to a data quality issue, but they are not sure which table or transformation caused the failure. They want to investigate the pipeline's event logs to find the root cause. What should they do?

Use the Databricks CLI command 'databricks pipelines list-events' to list all events for the pipeline.

Open the Spark UI for the pipeline's cluster and look for the failed stage in the job details.

Query the system.pipeline.events table to retrieve all pipeline events and look for any 'FAILED' status.

Access the pipeline's event log via the Databricks REST API endpoint 'GET /api/2.0/pipelines/{pipeline_id}/events' and filter for 'data_quality' events.

A data engineer is troubleshooting a Spark job that is failing due to out-of-memory (OOM) errors. They suspect that a specific stage is causing the issue due to a large shuffle. They want to confirm by looking at the stage's shuffle read metrics. However, the job has already failed and the Spark UI is no longer available. What should they do?

Query the system.compute.cluster_details table to see the cluster's memory configuration and infer if it was insufficient.

Use the Databricks REST API to retrieve the job run's output and look for OOM error messages.

Re-run the job with the Query Profiler enabled to capture the metrics.

Check the Spark event log for the failed run, which contains detailed metrics for each stage, including shuffle read size.

To retrieve audit logs of user actions, which system table should be queried?

`system.warehouse.warehouse_events`

`system.compute.cluster_metrics`

A data engineer notices that a specific job's cost has increased by 50% over the last month. They want to identify whether the increase is due to longer runtimes, more expensive cluster types, or more frequent runs. Which system tables should they query to get this information?

Query system.access.audit to see how many times the job was triggered and by whom.

Query system.billing.usage to get cost and usage per run, and join with system.jobs.runs to get run duration and frequency.

Query system.compute.cluster_details to see the cluster type and compare it with previous months.

Query system.query.history to see the SQL queries executed by the job and their durations.

Which Databricks system table would you query to analyze cluster utilization metrics such as CPU and memory usage?

`system.warehouse.warehouse_events`

`system.compute.cluster_events`

`system.compute.cluster_metrics`

DatabricksCertified Data Engineer Professional

Domain 5Objective 1

Monitoring DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 3)

Part of the Monitoring and Alerting 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 27 practice questions to prepare you well beyond it. (estimate)

27questions here

6free pages

8concepts

10%of the exam

Questions 11–15

11application · medium
A data engineering team uses Lakeflow Spark Declarative Pipelines to process streaming data. They notice that a pipeline occasionally fails due to a data quality issue, but they are not sure which table or transformation caused the failure. They want to investigate the pipeline's event logs to find the root cause. What should they do?
Select an answer first
12expert · hard
A data engineer is troubleshooting a Spark job that is failing due to out-of-memory (OOM) errors. They suspect that a specific stage is causing the issue due to a large shuffle. They want to confirm by looking at the stage's shuffle read metrics. However, the job has already failed and the Spark UI is no longer available. What should they do?
Select an answer first
13foundation · easy
To retrieve audit logs of user actions, which system table should be queried?
Select an answer first
14application · medium
A data engineer notices that a specific job's cost has increased by 50% over the last month. They want to identify whether the increase is due to longer runtimes, more expensive cluster types, or more frequent runs. Which system tables should they query to get this information?
Select an answer first
15foundation · easy
Which Databricks system table would you query to analyze cluster utilization metrics such as CPU and memory usage?
Select an answer first

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Monitoring DATA-ENGINEER-PROFESSIONAL Practice Questions (Page 3)

Questions 11–15

To retrieve audit logs of user actions, which system table should be queried?

A data engineer notices that a specific job's cost has increased by 50% over the last month. They want to identify whether the increase is due to longer runtimes, more expensive cluster types, or more frequent runs. Which system tables should they query to get this information?

Which Databricks system table would you query to analyze cluster utilization metrics such as CPU and memory usage?

Keep studying — Monitoring and Alerting