Tableau Desktop Foundations Practice Test

Selecting the Live option keeps Tableau connected directly to the source database. Each time a worksheet, dashboard, or filter needs data, Tableau submits a new query to PostgreSQL and retrieves the current results; no .hyper file or other local copy is created. Choosing Extract would instead copy the data into a compressed .hyper file on the local machine and direct all subsequent queries to that file. A live connection does not rely on a permanent cache, nor does it create or schedule refreshes automatically-those behaviors are specific to extracts that are published to Tableau Server or Cloud.

A highlight table is essentially a text table that encodes the measure by cell color. In Tableau, this is achieved by changing the mark type from Text (used in a plain crosstab) to Square, then applying a color palette. Bar or Line marks draw shapes that would hide the numbers, and leaving the mark type on Automatic keeps the view as a standard crosstab without cell shading.

Tableau supports cross-database joins in either live or extract mode. When you join a single SQL connection with a file-based connection such as Excel, the data source stays live; Tableau automatically decides at query time whether to execute the join in the SQL database (by loading the file data into a temporary table) or in the Hyper engine. Because the connection is already live, Tableau does not prompt you to create a permanent extract. Blending is unnecessary because both tables reside in one data source, and a single data source cannot mix live and extract modes.

An extract is a snapshot of the data stored in a highly-compressed .hyper file. Because the data are packaged inside the workbook or a separate extract file, users can open and query the dashboard without needing a direct network path back to the source system. This is particularly useful when the source database sits behind a corporate VPN that travelling or offline users cannot reach. By contrast, requirements for real-time data, preservation of database-enforced row-level security, or use of a local Excel file that is already accessible do not, by themselves, mandate an extract.

Dragging the Segment dimension to the Color shelf tells Tableau to break each bar into separate colored marks for every Segment value. Because the bars share the same axis and chart type, Tableau automatically stacks those marks on top of one another, producing a single bar per Region subdivided by Segment. Placing Segment on Size, Label, or Detail would add separate visual encodings or extra marks but would not instruct Tableau to stack the bars by category.

Because relationships defer physical joins until they are required, Tableau will query only the tables that contribute fields to the view. Since the visualization contains fields exclusively from Orders, Tableau sends a single-table query to Orders and does not join to Returns. A join (inner, left, or otherwise) would be generated only after a field from Returns is added to the view. Tableau never materializes a combined table when using relationships-data remains in the original tables until queried.

The Bar Chart option in Show Me becomes active only when the view contains at least one dimension and at least one measure. This combination lets Tableau determine the discrete categories (dimension) along one axis and the quantitative values (measure) that determine the bar lengths. Selecting only measures, only dimensions, or a single geographic dimension without a measure will leave the Bar Chart icon unavailable because Tableau lacks either the categorical or quantitative information required to draw bars.

Choosing Show Filter on the field's context menu places a filter card on the worksheet, giving users an interactive list, slider, or other control to change the filter values. Add to Context changes filter order but does not display a control. Show Parameter Control only works for parameters, not filters. Show Highlighter adds a highlighting control, not a filter selector.

A Tableau extract stores a snapshot of the source data in a local .hyper file. Because the workbook reads from this file instead of sending every query to the operational database, dashboard interaction is typically faster and the production system experiences less load. Live connections, in contrast, run queries directly against the source each time a user opens or interacts with the view, preserving real-time data and database-level security but increasing query traffic. Therefore, using an extract is the most appropriate choice for the stated scenario, while the other options describe characteristics or advantages of live connections that are not required here.

The Manual option in the Sort dialog disables automatic sorting rules and exposes a list of the dimension's members. You can then drag individual values up or down to create a custom sequence such as "Phones", "Accessories", followed by the remaining categories. The other options-Data Source Order, Alphabetic, and Field-determine order automatically based on source order, alphabetic order, or the values of another field, so they do not allow interactive re-ordering.

A density map is created by switching the mark type to Density. Tableau then replaces individual symbols with colored kernels that aggregate nearby points, showing areas of higher concentration. Changing the map style, rearranging pills, or picking Heat Map in Show Me will not activate the density kernel-only the Density mark type does.

When two continuous measures are placed on the Columns and Rows shelves without additional fields, Tableau automatically builds a scatterplot. The Marks card switches to the Circle mark type, displaying each data point as a circle positioned by its Sales (x-axis) and Profit (y-axis) values. Other mark types-such as Line, Gantt Bar, or Text-are only selected by default when the shelf arrangement suggests time-series, duration, or text tables, respectively, so they would not appear in this scenario.

The setting named "Aggregate for visible dimensions" tells Tableau to collapse the underlying rows down to the dimensions that are currently used in the view and then store only the aggregated results in the .hyper file. Because far fewer rows are written, the extract is smaller and refreshes more quickly. Options like "Include hidden fields", "Use Data Interpreter", or "Enforce referential integrity" do not perform row aggregation; they either add additional metadata to the extract or prepare the data in other ways, so they would not significantly shrink the file through summarization.

A .TDS file stores only the metadata of a data source-connection details plus customizations such as renamed fields, field aliases, default properties, and calculated fields. Because no visual or data-bearing content is saved, worksheet formatting (including custom color legends), individual worksheets, and any extracted rows are not included. Therefore, another user who connects to the .TDS will see the calculated field exactly as it was created, but none of the other listed items.

The Abc icon indicates a string data type. To make Tableau treat the values as numbers you must explicitly change the data type. This is done by right-clicking the field (or clicking the icon) in the Data pane, choosing Change Data Type, and selecting a numeric option such as Number (decimal). Dragging the field to a shelf, converting measures to dimensions, or editing the default number format does not alter the underlying data type, so these steps would not enable numeric aggregation.

Tableau lets you specify a geographic role so it can automatically generate latitude and longitude and plot values. You do this directly from the field: right-click the field in the Data pane, choose Geographic Role, and then select the appropriate role such as State/Province. Choosing options under Default Properties, changing the data type icon, or simply dragging the field onto a shelf does not set a geographic role, so those actions will not enable mapping or produce the generated latitude and longitude fields.

When two measures are placed on separate axes, Tableau overlays them but keeps independent scales. To complete a dual axis chart that is readable, you generally synchronize the secondary axis with the primary. This is done by right-clicking the secondary axis and choosing "Synchronize Axis." Doing so aligns the numeric scales and keeps the measures correctly overlaid. Simply selecting Dual Axis from the Analysis menu is not valid once the view already shows two axes; grouping the fields or changing the Marks card default will not synchronize the scales.

Because Order Date is currently discrete (blue pill), Tableau treats every date as a separate category and draws one mark per day. Converting the field to continuous (green pill) changes the x-axis to a continuous timeline and the default mark type to Line, so the points are connected. Changing the aggregation of Sales, adding a running total, or showing missing values does not alter the discrete nature of the date field, so none of those actions would produce one continuous line.

A combined axis chart places two or more measures on the same continuous axis. In Tableau, this is created by dragging the second measure directly onto the existing axis until the cursor changes to a double ruler icon, then dropping it. This action tells Tableau to use the same axis for both measures, typically by employing the Measure Names and Measure Values fields to create a view with a shared scale. Duplicating a pill to the Label shelf only adds text labels and does not build the chart. Choosing Dual Axis creates a separate, second axis for the new measure, which is a different chart type. Moving measures to the Measure Values shelf is the underlying structure for a combined axis, but it is not the most direct drag-and-drop action onto an existing axis as described in the question.

The correct method to create a single Tableau data source with data from different databases, like SQL Server and Excel, is to add a second connection on the Data Source page. This creates a multi-connection data source, allowing tables from both sources to be combined using relationships or joins. Data blending is different, as it combines data from separate data sources at the worksheet level, not within a single data source. A union is used to append rows of data from tables with similar column structures. Converting to an extract affects performance and data portability but is not a prerequisite for adding a new connection.