Databricks Databricks-Certified-Data-Engineer-Associate Exam With Confidence Using Practice Dumps

Serverless compute for workflows is designed to minimize operational overhead for Databricks Jobs while enforcing Unity Catalog governance. When a job is configured to run on serverless compute (in supported regions), Databricks automatically selects instance types , manages autoscaling , enables Photon where applicable, and handles runtime upgrades without user intervention. This directly satisfies the requirement to reduce operational toil. All-purpose clusters (option B) and traditional job clusters (option C) still require users to manage cluster sizing, instance families, and runtime versions, which contradicts the goal of automation. SQL warehouses (option A) are intended for SQL workloads and do not support running general Python notebook job tasks as workflow steps. Databricks documentation specifies that serverless workflows are compatible with Unity Catalog and are the recommended approach when users want fully managed compute for scheduled pipelines and tasks, provided the workspace is in a supported region. This makes serverless compute the correct and fully compliant solution.

 In Databricks SQL, you can use scheduled query executions to update your dashboards or enable routine alerts. By default, your queries do not have a schedule. To set the schedule, you can use the dropdown pickers to specify the frequency, period, starting time, and time zone. You can also choose to end the schedule on a certain date by selecting the End date checkbox and picking a date from the calendar. This way, you can ensure that the query does not run beyond the first week of the project’s release and does not incur any additional cost. Option A is incorrect, as setting a limit to the number of DBUs does not stop the query from running. Option B is incorrect, as there is no option to end the schedule after a certain number of refreshes. Option C is incorrect, as there is a way to ensure the query does not cost the organization money beyond the first week of the project’s release. Option D is incorrect, as setting a limit to the number of individuals who can manage the query’s refresh schedule does not affect the query’s execution or cost. References: Schedule a query, Schedule a query - Azure Databricks - Databricks SQL

The metrics indicate data skew because one task is taking substantially longer than most tasks in the stage. Databricks advises checking the maximum task duration against the 75th-percentile duration: when the maximum is at least 50% higher, the stage may be skewed. Here, the 75th percentile is 50 seconds and the maximum is 80 seconds, so the maximum is 60% higher. The 25th percentile, median, and 75th percentile are tightly grouped between 40 and 50 seconds, while the maximum is a clear outlier. That pattern means a small number of tasks are processing disproportionately expensive partitions. These values alone do not prove cluster under-provisioning or excessive garbage collection; those diagnoses require resource, executor, spill, and GC metrics. Therefore, option A is correct.

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