A data engineer manages dozens of scheduled Data Factory pipelines, Dataflows Gen2, and Spark notebooks across a single Microsoft Fabric workspace. Each morning they need one place that lists the recent runs of all of these item types together, with status and start time, so they can quickly spot any that failed overnight without opening each item individually. Which Fabric feature should they use?
- AOpen 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.
- BInstall the Microsoft Fabric Capacity Metrics app and read its timepoint detail page to see which scheduled items ran and whether any of them failed overnight.
- CCreate a Data Activator reflex that watches each pipeline and raises an alert, then review the alert history every morning to learn which runs failed.
- DOpen the Monitoring hub, which lists recent runs of pipelines, Dataflows Gen2, and notebooks together with their status and start time so failures are visible in one view. Correct
Why A 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.
Why B is wrong: The Capacity Metrics app reports compute consumption and throttling against a capacity, not a consolidated success or failure list of individual runs, so it is the wrong tool for spotting failed overnight jobs.
Why C is wrong: Data Activator can alert on conditions, but building and maintaining a reflex per item is heavier than needed and is forward-looking alerting rather than the consolidated run list the engineer asked to review each morning.
Why D is correct: The Monitoring hub aggregates run activity across item types in one filterable list with status and timing, which is precisely the single cross-item view needed to spot overnight failures quickly.