Databricks-DA-Assoc Creating Dashboards and Visualizations Practice Question
A Databricks SQL dashboard uses a counter visualization to display total revenue for the current month. The analyst notices that the counter shows a value even when the underlying query returns zero rows for the selected filter. Which behavior explains this and what should the analyst do to make the counter reflect the empty result set correctly?
⚠ Common exam trap
The trap here is jumping to SQL-level fixes like COALESCE or COUNT(*) when the symptom originates in the visualization's empty-state or default-value configuration.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The counter is configured to display the last non-null value or a default when the result set is empty; the analyst should adjust the counter's empty-state or default-value setting.
When a counter's dataset returns no rows, the aggregate value is NULL, and Databricks SQL counters can be configured with a default or fallback display that produces the misleading number. The correct remedy is to review the counter's visualization settings, including any default value or empty-state option, and align them with the intended behavior so an empty result is shown honestly rather than replaced by a placeholder.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The counter is using a COUNT(*) aggregation, which returns 0 for empty sets; the analyst should switch to SUM(revenue).
Why it's wrong here
COUNT(*) on an empty result set returns 0, which is a legitimate numeric value and would display as zero, not as a misleading non-zero value. Switching to SUM(revenue) would return NULL for an empty set, which is a different problem. The described symptom, a value shown when there should be none, is not explained by COUNT(*) behavior, so this diagnosis is incorrect for the scenario.
- ✗
The dashboard cache is serving a stale result; the analyst should disable caching for the warehouse.
Why it's wrong here
Disabling the warehouse cache affects performance broadly and does not address a counter that shows a value for an empty filter. Caching returns previously computed results but would not fabricate a number for a filter that has never returned rows. The described symptom is a visualization-level display behavior, so changing warehouse caching is both ineffective and unnecessarily disruptive.
- ✗
The counter is aggregating a column that contains NULLs; the analyst should wrap the measure in COALESCE to force a zero.
Why it's wrong here
COALESCE replaces NULL with a fallback value, but that would not explain why a value appears when the filter matches no rows. If the query truly returns no rows, the aggregate over an empty set yields NULL, and COALESCE would display the fallback rather than the correct absence of data. This action could actually mask the real issue and produce a misleading zero instead of an honest empty state.
- ✓
The counter is configured to display the last non-null value or a default when the result set is empty; the analyst should adjust the counter's empty-state or default-value setting.
Why this is correct
Counter visualizations in Databricks SQL can carry display defaults or retain a prior value when the underlying dataset returns no rows, which is why a stale or placeholder number appears. The fix is in the visualization configuration, not the SQL, so the analyst should inspect and clear the default or empty-state behavior so the counter shows no data or an explicit zero as intended.
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Databricks exam blueprint
This Databricks-DA-Assoc practice question is part of Courseiva's free Databricks certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the Databricks-DA-Assoc exam.