TDA-C01 Sample Questions & Answers

This is the most robust solution. By performing a left join in the data source pane with a non-equi join condition (HolidayDate >= ShipmentDate AND HolidayDate <= DeliveryDate), you can identify all holidays that fall within a shipment's transit time. The DATEDIFF('weekday', ...) function correctly calculates the number of weekdays between two dates. Finally, subtracting the distinct count of holidays (COUNTD([HolidayDate])) from the weekday count gives the precise number of business days. This approach offloads the complex logic to the database layer and is more scalable than complex IF statements or data blending for this row-level task.

When Category and Sub-Category are placed on the shelves (e.g., Rows), Tableau partitions the view into 'panes' for each Category. A quick table calculation for 'Percent of Total' with its scope set to 'Pane' will calculate the sum of sales for each Sub-Category as a percentage of the total sales within its pane (i.e., its parent Category). 'Table (Across)' would calculate the percentage based on the grand total for the entire table. An LOD would also work, but the table calculation is more direct for this specific visual layout.

The most efficient method is to add the filter from one of the worksheets to the dashboard. Then, by accessing the filter's context menu (the dropdown arrow), you can specify its scope. Choosing 'All Using This Data Source' will make this single filter control every other worksheet on the dashboard that is built from the same data source. This avoids creating redundant filters or complex filter actions.

The diagram accurately represents Tableau's filter order of operations. Filters are applied sequentially from the broadest to the most specific. Extract filters are applied first when the extract is created. Data source filters are next, restricting data at the source level. Context filters create a temporary table that all subsequent filters run against, so they come next. Finally, standard dimension filters are applied before the view is rendered.

Mobile users are accustomed to scrolling. A single vertical layout is easier to navigate on a small screen than a wide, multi-column layout.

To provide information quickly, key metrics and summaries should be visible without requiring the user to scroll down.

Screen real estate is limited on mobile devices. Minimizing filters and making the controls larger (e.g., single value lists instead of compact lists) improves usability.

This is a two-step process best handled on the Data Source page. First, use the custom Split function to parse the comma-separated string into multiple columns. Second, select these new columns and use the Pivot function. This transforms the data from a wide format (multiple columns of keywords) to a tall format (a single column containing all keywords), which allows you to easily count the occurrences of each unique keyword.

The most direct method is to use the Analytics pane. By dragging a Reference Line onto the plot for the Sales axis and setting its value to Average, you create a vertical line at the average sales value. Repeating this for the Profit axis creates a horizontal line at the average profit. These two lines intersect to form the four desired quadrants visually.

Assuming the view is structured with a continuous monthly date on an axis, the LOOKUP table calculation is the correct tool. LOOKUP(SUM([Sales]), -12) will fetch the sales value from 12 positions (months) prior in the partition. This directly accesses the value from the same month in the previous year. Dividing the current sales by this looked-up value provides the year-over-year comparison. The other options either look back one position (previous month) or use incorrect logic for this specific requirement.

This is true. A context filter is processed before other filters in the view (except extract and data source filters). Tableau creates a temporary, smaller dataset based on the context filter's selection. All subsequent filters and calculations then run against this smaller dataset, which can lead to significant performance improvements, especially on large data sources and complex dashboards.

The most straightforward method is to select the map worksheet on the dashboard canvas and click the 'Use as Filter' icon (which looks like a funnel) in its border. This automatically creates a dashboard filter action where the map is the source sheet and all other sheets (including the bar chart) are the target sheets. Clicking a state on the map will then pass that state as a filter to the bar chart.