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| Section | Objectives |
|---|---|
| Topic 1: Connecting to Data | - Data preparation basics
|
| Topic 2: Understanding Tableau Concepts | - Core Tableau terminology
|
| Topic 3: Sharing Insights | - Dashboards
|
| Topic 4: Exploring and Analyzing Data | - Building views
|
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NEW QUESTION # 23
A Tableau associate has the following data.
The associate wants to sort each category's sub-category by ascending sales.
Which sort option should the associate use?
Answer: D
Explanation:
The correct option is Nested because the Sub-category members must be sorted independently within each Category based on Sales. A nested sort evaluates the measure separately inside each pane or parent dimensional grouping. In this example, the Home sub-categories should be sorted according to their Home Sales values, while the Bath sub-categories must be sorted independently according to the Bath Sales values.
For Home, Bulk has Sales of $50 and Accessory has $60, so ascending order is Bulk followed by Accessory.
For Bath, Accessory has $45 and Bulk has $75, so ascending order is Accessory followed by Bulk. This demonstrates why a single global ordering is insufficient: the correct Sub-category order differs between the two Category groups. A non-nested field sort can produce a consistent order based on aggregated values across the view, which would not satisfy the requirement to rank Sub-category independently inside each Category. Manual sorting would require explicit maintenance, while data source order simply follows the ordering supplied by the underlying source. Tableau's official sorting documentation states that nested sorting considers values independently per pane and sorts the values separately within those panes.
NEW QUESTION # 24
A Tableau associate has a data set that builds a union between two tables. The associate needs to extract the data set.
What should the associate use to extract the data set?
Answer: A
Explanation:
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.
NEW QUESTION # 25
Which string function returns a Boolean value as a result?
Answer: A
Explanation:
STARTSWITH is the function that returns a Boolean result, so D is correct. The function evaluates whether one string begins with a specified substring. Its conceptual syntax is STARTSWITH(string, substring). If the original string begins with that substring, Tableau evaluates the expression as TRUE; otherwise, it evaluates as FALSE. For example, STARTSWITH([Customer Name], "A") returns TRUE for values beginning with A and FALSE for other values. Because the output is Boolean, STARTSWITH is particularly useful in calculated fields, conditional expressions, filters, classifications, and data-quality logic. The other listed functions return different data types. CHAR converts a numeric character code to its corresponding character and therefore returns a string. FIND searches for the position of a substring and returns a numeric position.
UPPER converts the supplied text to uppercase and returns another string. Consequently, only STARTSWITH directly produces the TRUE/FALSE result required by the question. This distinction is fundamental when selecting functions for Boolean calculations because Tableau evaluates Boolean fields differently from numeric and string-returning expressions.
NEW QUESTION # 26
Which two elements can have their values changed by using a dashboard action?
Answer: A,D
Explanation:
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.
NEW QUESTION # 27
Which chart type uses automatically generated sizes that cannot be resized by using the Marks card?
Answer: D
Explanation:
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.
NEW QUESTION # 28
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