Comprehensive and Detailed Explanation From Exact Extract:

The dataset contains timestamps for each individual user interaction. Growth in user adoption over 6 months means the number of rows has expanded significantly. Tableau’s performance documentation states that large row-level datasets can cause performance degradation even when using extracts, especially when:

The visualization is aggregated to a higher level (such as daily), and

The underlying extract still contains much more granular data than needed.

Tableau recommends pre-aggregating data before it reaches Tableau Desktop, which reduces extract size, memory use, and query time. This improves performance without changing what the visualization displays.

Option D uses Tableau Prep, which is included with Tableau Creator licensing and therefore incurs no additional cost. Tableau Prep can aggregate raw timestamp data into daily totals per product feature, which matches the visualization’s actual granularity. This results in:

A dramatically smaller extract

Faster queries

No change to how the dashboard looks or functions

Option A would remove product features from the visualization, altering the dashboard content and reducing insight, which does not meet the requirement of minimal impact.

Option B requires purchasing an external ETL tool, which violates the requirement of no additional cost.

Option C reduces the number of extract refreshes but does not improve dashboard performance; the data would remain equally granular and equally slow.

Therefore, Tableau Prep aggregation is the correct solution that improves performance while maintaining the same visualization and incurring no additional cost.

Tableau performance guidelines recommending pre-aggregation of highly granular datasets.

Tableau Prep documentation stating it can be used to aggregate data before creation of extracts.

Tableau’s extract optimization guidance describing how reducing row counts improves query and visualization performance.