Analytics-DA-201 - Connect to and Transform Data (24% of the exam) - Section 1.3
Choose and apply Tableau Prep transformations - unions, joins, aggregations, filtering, and pivots - and select the appropriate flow output type for a business scenario.
Practice question for this objective
lock_openFree sampleConnect to and Transform Datamedium
A Tableau Prep flow at Brindle Freight cleans a daily shipments file and must deliver the cleaned result to two audiences: a fleet of Tableau workbooks that refresh from a single curated source on Tableau Cloud, and a finance reporting tool outside Tableau that reads a table in the company analytics database. The analyst wants both consumers served from the one flow without each Tableau workbook re-running the cleaning logic. Which two flow output types should the analyst configure? Select TWO.
AA published data source output on Tableau Cloud, so many workbooks share one curated source that they refresh from on a schedule.check_circle Correct
BA database table output that writes the cleaned rows into the analytics database, so the external finance tool can read the table directly.check_circle Correct
CA local .hyper extract saved to the analyst's own laptop, so each Tableau workbook opens that single file from disk during analysis.
DA live connection from every workbook straight to the raw shipments file, so each book applies the cleaning steps itself at view time.
EA flow output saved as a packaged workbook, so the finance tool and the Tableau books both open the cleaned data from that one file.
Select the appropriate Tableau Prep flow output types to serve both Tableau workbooks and external tools from a single scheduled flow. Prep can write several output types from one flow. A published data source on Tableau Cloud gives many workbooks one curated source they refresh on a schedule, while a database table output lands the rows in the warehouse where non-Tableau tools can query them, so both audiences are served without any workbook rerunning the cleaning steps.
Why A is correct: A published data source lets multiple workbooks draw from one curated source and refresh on a schedule without rerunning the flow.
Why B is correct: A database table output writes the result into the warehouse where non-Tableau tools such as the finance reporting tool can query it.
Why C is wrong: A local extract on one laptop is not shareable on a schedule across many workbooks, so it fails the curated shared-source need.
Why D is wrong: A live connection to raw data forces every workbook to redo the cleaning, which is exactly what the analyst wants to avoid.
Why E is wrong: A packaged workbook is not a Prep flow output type and cannot be read as a table by an external finance tool.
Answers that look right on this material and are not. Each one is a distractor from a different question in the Analytics-DA-201 bank for this domain.
Write the output to a local .hyper file on the analyst's machine and share it with each workbook author
errorWhy it is wrong: A local file is tempting because the flow can produce a .hyper extract, but it cannot be refreshed centrally on a schedule for shared workbook access, so it fails the requirement.
Leave all rows in the flow and apply the depot and date filters in the workbook after the output has been generated.
errorWhy it is wrong: This seems convenient because the workbook can filter, but it forces the whole flow and output to carry every unwanted row, wasting processing and storage rather than improving flow efficiency.
Place OrderID in Grouped Fields alongside StoreID, so the step still returns one summarised row for every individual store.
errorWhy it is wrong: Adding OrderID to Grouped Fields makes the grain one row per order per store, defeating the per-store output requirement.