Databricks-Certified-Professional-Data-Engineer Exam Dumps : Databricks Certified Data Engineer Professional Exam

The Databricks Jobs REST API provides several endpoints for automation. The correct monitoring and rerun flow uses three specific calls:

GET /api/2.1/jobs/list – Lists all available jobs within the workspace.

GET /api/2.1/jobs/runs/list – Returns all runs for a specific job, including their current state (e.g., TERMINATED: FAILED).

POST /api/2.1/jobs/run-now – Immediately triggers a rerun of the specified job.

This sequence aligns with Databricks’ prescribed automation model for job observability and recovery. Using jobs/update modifies metadata but does not rerun jobs, and jobs/create is only used for creating new jobs, not rerunning failed ones. Cancelling and recreating jobs introduces unnecessary duplication. Therefore, option A is the correct automated recovery workflow.

The Query Profiler in Databricks SQL and notebooks provides a detailed breakdown of query performance metrics. The “Total wall-clock duration” metric directly represents the total elapsed time from query start to completion, including all execution, planning, and waiting stages. In contrast, “Aggregated task time” reflects the cumulative duration across all parallel tasks, which does not equal the total elapsed wall time since tasks often run concurrently. Using job duration from Spark UI can underestimate or overestimate runtime when queries span multiple jobs. Therefore, the Query Profiler’s total wall-clock duration is the officially documented method to determine actual query execution time.