Databricks-DA-Assoc Creating Dashboards and Visualizations Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Embed the quarter value directly in each query's WHERE clause and republish the dashboard whenever the quarter changes.

  Why it's wrong here

  Hardcoding the quarter in each query's WHERE clause removes interactivity entirely; viewers cannot change the value without editing and republishing the dashboard. This defeats the purpose of a self-service executive dashboard and creates a bottleneck on the analyst. It also risks inconsistency if some queries are updated and others are not. This is not a valid approach for enabling viewer-driven filtering across visualizations.

• ✓

  Add a dashboard-level filter for the fiscal quarter field, ensuring each visualization's dataset exposes a field with the same name and type so the filter applies to all of them.

  Why this is correct

  Dashboard-level filters apply to all visualizations whose datasets contain a matching field. When each dataset exposes a fiscal quarter field with the same name and compatible type, a single filter selection propagates to every relevant widget. This gives viewers one control that updates multiple visualizations consistently, which is exactly the cross-visualization behavior requested for the executive review.

• ✓

  Create a parameter in each query that references the same parameter name, add a parameter widget to the dashboard, and use it to drive the value across the queries.

  Why this is correct

  Databricks SQL dashboards support parameters that can be shared across multiple queries when they use the same parameter name and are marked as dashboard parameters. Adding a parameter widget lets the viewer select a value once, and all queries referencing that parameter update accordingly. This provides consistent cross-visualization filtering for a single value such as a fiscal quarter, making it a valid approach.

• ✗

  Use a cross-filter from a table visualization that lists quarters, relying on the dashboard to propagate the selection to all other widgets regardless of their datasets.

  Why it's wrong here

  Cross-filtering propagates a selection only to visualizations whose datasets share the same field used in the selection. It does not automatically apply to all widgets regardless of their underlying data, so visualizations without a matching quarter field would not update. This makes the approach unreliable for ensuring consistent filtering across every relevant visualization in the executive dashboard.

• ✗

  Duplicate each visualization and create separate copies for each quarter, then instruct viewers to switch between dashboard tabs manually.

  Why it's wrong here

  Duplicating visualizations for every quarter creates maintenance overhead and does not provide interactive filtering. Viewers would have to manually navigate between tabs, which is error-prone and does not update all relevant visualizations from a single selection. It also multiplies the number of queries and widgets, increasing dashboard load time. This approach does not meet the requirement for a single control that consistently filters multiple visualizations.