The core requirement is to update the dashboard's visuals within 10 minutes of new data arriving in the gold-level tables. The mechanism designed for this purpose in Databricks is the dashboard refresh schedule. By setting a refresh schedule with an interval of 10 minutes or less, the queries that power the dashboard will automatically re-run at the specified frequency. This ensures that any new data populated by the streaming pipeline into the gold tables is promptly reflected in the dashboard, meeting the stakeholders' timeliness requirement.

B: An always-on SQL Warehouse reduces query start-up latency but does not trigger the refresh itself. The refresh timing is controlled by the schedule interval, not the warehouse's availability.

C: Including stakeholders as subscribers sends them a static snapshot via email. It is a distribution feature and does not control the data freshness of the live dashboard itself.

D: A Structured Streaming cluster is used for the data ingestion pipeline that populates the tables. Dashboard queries are executed on a SQL Warehouse, not a streaming cluster.

1. Databricks Documentation

"Schedule a dashboard for automatic refreshes": This document explicitly states

"You can schedule a dashboard to automatically refresh at a specified interval. When a dashboard is scheduled

Databricks runs the queries that back the dashboard visualizations and updates the visualizations with the latest data." This directly supports setting a scheduled interval to meet the requirement.

Source: Databricks Official Documentation > SQL > Dashboards > Schedule and share dashboards.

2. Databricks Documentation

"What are SQL warehouses?": This documentation describes SQL warehouses as the compute resources for running queries in Databricks SQL

including those for dashboards. It distinguishes the warehouse's role (compute execution) from the scheduling mechanism. The configuration of a warehouse (like auto-stop

which relates to the "always-on" concept) affects performance

not the trigger for a refresh.

Source: Databricks Official Documentation > SQL > Administration > What are SQL warehouses?.

3. Databricks Documentation

"Structured Streaming": This section details that Structured Streaming is a stream processing engine for building data pipelines. This confirms it is the technology used to populate the gold tables

not the technology used to query them for a dashboard.

Source: Databricks Official Documentation > Development > Structured Streaming.