Free Salesforce Certified Tableau Desktop Foundations Analytics-101 Exam Questions

Tableau dashboard actions can directly change Parameters and Sets, so B and C are correct. A parameter action allows a user to modify a parameter value by interacting directly with marks in a visualization. For example, selecting a mark can assign the mark's Sales value to a parameter, which can subsequently drive a calculated field, reference line, filter logic, or another analytical component. Tableau provides the dashboard action type Change Parameter specifically for this purpose. A set action similarly modifies the membership of an existing set based on interaction with marks. The action can assign selected values to the set, add values, or remove values. This enables sophisticated interactive behavior such as proportional brushing, dynamic comparisons, drill-down effects, and conditional calculations. Tableau identifies this action type as Change Set Values. Groups and bins do not have equivalent dashboard action types that dynamically modify their definitions through viewer interaction. They are data organization constructs configured during authoring. The Tableau Actions documentation explicitly lists Change Parameter and Change Set Values among supported action types. Parameter Actions documentation confirms that visualization interaction can update the target parameter, while Set Actions documentation confirms that interactions can modify values contained in a target set.

The correct configuration is Logical tables that use a single table extract, making C correct. In Tableau's data model, a union operates in the physical layer. The two unioned physical tables are merged into one resulting logical table. Tableau explicitly describes logical tables as containers that can contain multiple physical tables joined or unioned together. When an extract is stored using Logical Tables, Tableau creates one extract table for each logical table in the data source. Because the scenario contains one logical table composed of the two unioned physical tables, the extract is consequently represented as a single extract table. This is different from using Physical Tables storage. Physical storage retains an extract table for each physical table and is subject to additional eligibility restrictions. Tableau's current terminology replaced the older 'Single Table' and 'Multiple Tables' extract terminology with Logical Tables and Physical Tables, respectively. Official Tableau documentation states that physical tables joined or unioned inside a logical table are merged as part of that logical table, and that Logical Tables extract storage creates one extract table per logical table. Therefore, a two-table union forming one logical table corresponds to a logical/single-table extract structure.

For a standard custom latitude field, Tableau recommends a Number (decimal) data type and a Measure data role. Therefore, A and B are correct. Latitude represents a numeric coordinate, including fractional values such as 40.7128, so Number (decimal) is the appropriate data type. Treating latitude as a measure enables Tableau to position coordinate values continuously on an axis rather than interpreting each coordinate as an independent categorical member. Tableau's official mapping guidance states that, in most cases, latitude and longitude fields should have a data type of Number (decimal), a data role of Measure, and the corresponding Latitude or Longitude geographic role. When used in a map, Longitude is normally placed on Columns and Latitude on Rows as continuous measures. This differs from geographic fields such as Country, State, or City, which are usually string dimensions. Although specialized mapping scenarios can convert latitude or longitude to dimensions, that is not the standard configuration addressed by this question. The official Tableau mapping documentation explicitly specifies the Number (decimal) + Measure configuration for latitude and longitude fields when preparing geographic data for mapping.

The correct answer is Treemaps. A treemap represents data as a set of rectangles. Tableau uses one or more dimensions to define the rectangular structure, while a measure placed on Size determines each rectangle's relative area. Tableau automatically lays out the rectangles to use the available visualization space efficiently. The analyst therefore controls relative size through the measure encoded on Size rather than manually resizing the generated rectangles with the ordinary Marks card size slider. For example, if Sub-Category defines the rectangles and Sales is placed on Size, categories with larger aggregated Sales values receive proportionally larger rectangles. Changing the Sales values or replacing the field on Size changes the relative areas, while Tableau recalculates the layout automatically. Pie charts have mark-size controls and can be resized through the Marks card. Area charts derive their visual area from their axes and underlying measures. Histograms use bars representing bins and frequencies and have different bar-sizing behavior. Tableau's official Treemap documentation identifies Size and Color as the crucial encodings and explains that a measure on Size determines the areas of the rectangles. The resulting treemap geometry is automatically laid out according to those values rather than manually sized mark by mark.

Dimensions are fields Tableau uses primarily to categorize, segment, and describe data. They typically contain qualitative information such as names, geographic locations, categories, dates, and identifiers. Therefore, C is correct. Tableau also states that dimensions affect the level of detail in a visualization. Adding another dimension can partition existing marks into more granular groups because Tableau incorporates the field's members into the view's dimensional structure. Therefore, B is correct. The dimension/measure classification is independent from the discrete/continuous classification. Although dimensions are commonly discrete, a dimension can also be continuous. Date fields are a standard example: a date may be represented as a discrete date part or a continuous date value. Therefore, E is correct. Option D instead describes measures, which generally contain quantitative numeric values and are aggregated. Option A is not an inherent defining characteristic of dimensions; formatting behavior depends on how a field is used and whether it is discrete or continuous. Official Tableau guidance identifies dimensions as qualitative fields that group or segment data and explicitly states that dimensions affect the level of detail in the view.

Dimension filters support Multiple Values (drop-down) and Wildcard Match, making A and C correct. Dimensions typically contain categorical values. When a dimension filter is displayed as an interactive filter card, Tableau provides several categorical selection modes. Multiple Values (drop-down) presents dimension members in a compact drop-down control where the viewer can select multiple categories simultaneously. This is useful when a field contains several discrete members but screen space needs to be conserved. Wildcard Match provides a text input field that dynamically selects dimension members whose text matches a supplied character pattern. Tableau supports the asterisk as a wildcard. For example, entering tab* can match values beginning with 'tab.' This mode is appropriate for searchable text-based categorical dimensions. By contrast, Range of Values is associated primarily with continuous quantitative filters, where users specify minimum and maximum values. At most is likewise an open-ended quantitative/date filtering mode used to establish an upper boundary rather than a categorical dimension-selection mode. Tableau's official filtering documentation explicitly lists Multiple Values (Dropdown) and Wildcard Match among the available filter card modes for dimensions. It separately describes quantitative range and open-ended filtering behavior for measures and continuous fields.