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Implementing Data Engineering Solutions Using Azure Databricks

Topic 1

Question 77

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A production Lakeflow Job orchestrates five tasks that run in sequence: `bronze_ingest` → `silver_clean` → `gold_aggregate` → `publish_dashboard` → `notify_team`. During last night's run, `bronze_ingest`, `silver_clean`, and `gold_aggregate` all completed successfully (each writing to a Delta table that took 40 minutes of compute), but `publish_dashboard` failed because a downstream BI warehouse was temporarily offline. As a result, `notify_team` was skipped. The warehouse is now back online. You need to complete the failed run **without recomputing the three expensive upstream tasks** and while preserving the matrix view history of the original run. You open the **Job run details** page for the failed run. Which action satisfies the requirement most efficiently?

  • AClick **Run now** to trigger a brand-new run of the entire job; all five tasks re-execute from `bronze_ingest`.
  • BClick **Repair run**, which re-runs only the unsuccessful task `publish_dashboard` and its dependent task `notify_team`, keeping the successful results of the upstream tasks.
  • CEdit the job to delete `bronze_ingest`, `silver_clean`, and `gold_aggregate`, then click **Run now** so that only the remaining tasks execute.
  • DClone the job, remove the three completed tasks from the clone, and run the clone; this avoids recomputation and keeps the original history intact.