DP-700 - Monitor and Optimize an Analytics Solution (33% of the exam) - Section 3.1

Monitor data ingestion and data transformation across Microsoft Fabric items.

Use the Monitoring hub and run history to track pipeline, Dataflow Gen2, and Spark notebook execution across Fabric items. Interpret refresh and run status in the capacity metrics app to identify bottlenecks affecting ingestion and transformation throughput.

Monitoring hub and run historypipeline and Dataflow Gen2 run monitoringSpark application and notebook monitoringcapacity metrics apprefresh and run status

Practice question for this objective

Free sampleMonitor and Optimize an Analytics Solutionmedium

A scheduled Dataflows Gen2 in Microsoft Fabric loads several Lakehouse tables and its overnight refresh failed. The engineer opens the run history and sees the refresh listed with a failed status, but needs to know which specific query inside the dataflow failed and the error text that query returned before correcting the offending step. Which detail in the Dataflows Gen2 refresh history most directly identifies the failing query and its error?

AThe per-query refresh details, which list each query in the dataflow with its own status and the error message returned for the query that failed. Correct
BThe capacity utilisation chart for the workspace, which shows whether the dataflow consumed enough compute units during the failed refresh.
CThe Lakehouse table lineage view, which shows which destination tables the dataflow writes to and how they connect downstream.
DThe workspace Git commit history, which records when the dataflow definition was last changed and by whom before the refresh failed.

Dataflows Gen2 refresh history records per-query status and error text, which is the direct path to the query that caused a refresh failure. A Dataflows Gen2 refresh evaluates each query and logs its outcome individually, so the per-query refresh details surface the exact failing query and the error it returned, unlike capacity, lineage, or Git views that hold no per-query runtime error.

Why A is correct: Dataflows Gen2 refresh history records the outcome of each query separately, so the per-query details point straight to the failing query and its error message.

Why B is wrong: Capacity utilisation can hint at throttling but does not name which query failed or return the query-specific error text the engineer needs to fix the step.

Why C is wrong: Lineage shows data flow relationships between items but carries no refresh status or error message, so it cannot reveal which query failed.

Why D is wrong: Commit history tracks definition changes for version control and contains no runtime refresh outcome, so it cannot identify the failing query or its error.

See more DP-700 practice questions, answers explained.

Exam traps in Monitor and Optimize an Analytics Solution

Answers that look right on this material and are not. Each one is a distractor from a different question in the DP-700 bank for this domain.

Open each pipeline, dataflow, and notebook in turn and read its own run history, since the hub cannot filter by run status.
Why it is wrong: Per-item history does show status, but it is the slow manual path the engineer wants to avoid, and the claim that the hub cannot filter by status is wrong.
Open the run history on each individual pipeline and notebook in turn, reading the per-item activity output to confirm whether the most recent overnight run completed.
Why it is wrong: Per-item run history does show that item's runs, but checking each item separately is exactly the manual, item-by-item effort the requirement rules out and gives no single consolidated view.
The Capacity Metrics app timepoint page, which lists the notebook's individual Spark stages and tasks along with their durations for the slow run.
Why it is wrong: The Capacity Metrics app reports capacity-level consumption and does not expose a notebook's Spark jobs, stages, and tasks, so it cannot show the per-stage timing needed to find the slow stage.

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