Plan and Prepare Data Connections22% of the exam
Extract aggregation, filters and incremental refresh
Set the aggregation level and refresh strategy for extracts and flows: Aggregate data for visible dimensions and date roll-up, the filter-aggregate-then-Top N order, full versus incremental refresh, the 2024.2 minimum date range and how Tableau counts it, the 3-decimal time precision trap, Aggregate Measures in views, and Prep Aggregate steps before output.
Specify aggregation level and strategy for data sources in Tableau products (Tableau Desktop, Tableau Prep, Tableau Cloud, Tableau Server).
~8 min read · 7 practice questions ·
What the exam expects
- Predict the rows an extract will hold with Aggregate data for visible dimensions, date roll-up, extract filters and Top N.
- Choose between full and incremental refresh, pick the column that identifies new rows, and know what blocks incremental refresh.
- Configure the 2024.2 minimum date range to refresh and calculate exactly which periods Tableau re-extracts.
- Explain duplicate rows caused by datetime precision beyond 3 decimal places.
- Reduce rows where the work happens: Aggregate Measures in a Desktop view, or an Aggregate step before a Tableau Prep output published to Tableau Server or Tableau Cloud.
Key terms:
- Aggregate data for visible dimensions: an extract option that stores measures at their default aggregation for the visible dimensions.
- Roll up dates: an aggregation option that truncates dates to a level such as Year or Month before aggregating.
- Incremental refresh: a refresh that adds rows newer than the last refresh, identified by one column.
- Minimum date range to refresh: the 2024.2 subrange setting that re-extracts recent, already extracted rows.
- Aggregate step: a Tableau Prep step that groups fields and aggregates others to change the flow's granularity.
Key facts
Row-count order
- Detail
- Filters and aggregation first, then All rows or Top N
Aggregation options
- Detail
- Visible dimensions; optional Roll up dates
Physical Tables storage
- Detail
- Disables extract filters, aggregation, Top N, sampling
Default refresh
- Detail
- Full refresh replaces all rows
Incremental prerequisites
- Detail
- All rows; no sample; no aggregation
Non-unique key column
- Detail
- Supported for incremental refresh from 2024.1
Minimum date range
- Detail
- 2024.2+; current unit plus N earlier units
Source structure change
- Detail
- Full refresh before incremental can resume
Time precision
- Detail
- Up to 3 decimal places in the data engine
Slow disaggregated view
- Detail
- Select Analysis > Aggregate Measures
Prep granularity
- Detail
- Aggregate step: Grouped Fields and Aggregated Fields
- Why can a Top N extract return fewer distinct positions than expected?
- When must a .tde extract be upgraded to .hyper?
- Where does Desktop show whether past refreshes were full or incremental?
- Which key column types offer the optional minimum date range?
The questions are free to read. Answers with their sources and self-grading are included with the pack.
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