Tableau Certified Data Analyst Practice Questions

This is the classic approach for cohort and transition analysis in Tableau. By creating a view with CustomerID on Detail and month on an axis, you can use table calculations. The LOOKUP(ATTR([SubscriptionTier]), -1) function, when computed along the date for each customer, correctly retrieves the subscription tier from the previous month. Placing the current tier on one shelf and this 'previous tier' calculation on the other allows you to build a matrix (crosstab) where you can count the distinct customers at each intersection, effectively showing the transitions.

Tableau manages custom color palettes through the Preferences.tps file located in the user's 'My Tableau Repository'. To add a new palette, you must edit this file and add a block with the desired name and the list of hex codes for the colors. After saving the file and restarting Tableau, the new palette will appear in the color selection dialog.

The most efficient place to apply the filter is within the Extract Data dialog itself. An extract filter is applied during the creation of the extract. This means that only the data matching the filter criteria (in this case, the last two years) is pulled from the source database and stored in the .hyper file. This results in a smaller extract, faster extract creation, and better overall workbook performance.

The core difference lies in their output. A group takes multiple members of a dimension and combines them into a new, single member (e.g., grouping 'Washington', 'Oregon', 'California' into a 'West Coast' group). A set, however, is a custom field that defines a subset of your data, resulting in a binary outcome: members are either 'IN' the set or 'OUT' of the set. This IN/OUT nature makes sets very powerful for use in calculations and dashboard actions.

This is the most common and direct method. Dragging a field like 'City' and dropping it on 'State' in the Data pane will prompt you to create a hierarchy.

This method allows you to create a hierarchy from multiple fields at once. You can CTRL-click (or Command-click on Mac) to select the desired dimensions, then right-click to access the 'Create Hierarchy' option.

The WINDOW_STDEV function is a table calculation specifically designed to compute the standard deviation over a moving window of data. The arguments (SUM([Stock Price]), -29, 0) correctly define the aggregation and the window, which includes the current day and the 29 preceding days, making a 30-day rolling period. STDEV() is a regular aggregation and calculates the standard deviation for the entire partition. The FIXED LOD would calculate the standard deviation for each stock ticker across all time, not a rolling window.

A live connection queries the underlying database directly. This is the ideal choice when data freshness is critical and the database is performant enough to handle the query load from dashboard interactions. An extract, even if refreshed frequently, introduces latency as it is a snapshot of the data. A published data source on Tableau Server still requires a connection type, and a data blend is for combining different data sources, not for ensuring data freshness.

Sets are necessary to group the selected category ('IN' the set) versus all other categories ('OUT' of the set). This grouping is fundamental to creating the two separate trend lines.

Set Actions provide the interactivity, allowing a user's click or selection on the dashboard to dynamically change the members of the set. This action will drive which category is considered 'selected' for the comparison.

This is true. Tableau Server itself cannot use the credentials of the viewing user to connect to a data source that requires integrated authentication. Instead, an administrator must configure a specific 'Run As User' account. This account has the necessary permissions to the database, and Tableau Server will use this single account for all queries to that data source, acting on behalf of the end-users.

A Join step is used to combine data from two tables based on a common field. In this case, both the order details and product information tables contain the ProductID field. By joining on this common field, the analyst can append columns from the product table (like ProductName and Category) to the rows of the order details table, creating the desired wide-format output. A Union stacks rows on top of each other, which is incorrect here.