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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Connecting to and Preparing Data | 23% | - Create live connections and extracts - Manage data properties - Create and manage the data model |
| Topic 2: Exploring and Analyzing Data | 37% | - Organize data and apply filters - Apply analytics to a worksheet - Create basic charts |
| Topic 3: Sharing Insights | 25% | - Create and modify a dashboard - Format view for presentation - View and share workbook data |
| Topic 4: Understanding Tableau Concepts | 15% | - Understand discrete and continuous fields - Understand aggregations - Understand dimensions and measures |
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NEW QUESTION # 20
Which two statements accurately describe relationships in Tableau Desktop?
Answer: A,C
Explanation:
Relationships are designed to preserve each table's level of detail and keep logical tables separate within the data source, making A and B correct. Unlike physical joins, relationships do not flatten participating tables into one combined row-level table when the data source is created. Tableau keeps each logical table distinct and evaluates the relationship dynamically based on fields actually used in a visualization. This architecture helps Tableau analyze data residing at different granularities while reducing the duplication and data-loss risks that can occur with fixed physical joins. Tableau's official relationship documentation explicitly states that relationships maintain each table's native level of detail and create independent domains at multiple granularities. The tables are not merged together in the data source. Option C describes the behavior of joins or unions in the physical layer. Physical tables can be combined to form one logical table, but this is not what a relationship does. Option D is also incorrect because relationships do not require the author to choose a fixed join type such as inner, left, right, or full outer. Tableau determines appropriate joins contextually during analysis. Relevant Tableau Desktop concepts include logical tables, physical tables, relationships, contextual joins, cardinality, and preservation of granularity.
NEW QUESTION # 21
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 # 22
At a minimum, what does a Tableau associate need to create a simple scatter plot?
Answer: A
Explanation:
A basic scatter plot requires one measure on Columns and another measure on Rows, so D is correct. A scatter plot displays the relationship between two quantitative variables. One measure determines the horizontal position of each mark, while the second measure determines its vertical position. For example, placing SUM(Sales) on Columns and SUM(Profit) on Rows establishes a two-dimensional quantitative coordinate system. A dimension may then be added to Detail, Color, Shape, or another Marks property to create separate marks for entities such as customers, products, or states. However, that additional dimension is not the fundamental axis requirement identified by the question. The minimum structural requirement is two quantitative measures defining the x- and y-axes. A dimension plus a measure normally produces a categorical view such as a bar chart rather than a scatter plot. Two dimensions likewise do not supply the pair of quantitative scales required to analyze numerical correlation or distribution. Tableau's visual construction model distinguishes continuous quantitative measures, which generate axes, from discrete fields, which generally create headers. A scatter plot relies on two quantitative axes and therefore requires measures on both Rows and Columns. This is consistent with Tableau's standard scatter-plot construction methodology and its continuous-field behavior.
NEW QUESTION # 23
A Tableau associate is creating a combined axis chart.
Where should the associate drag the second measure after dragging the first measure to the Rows shelf?
Answer: A
Explanation:
Because the first measure is on the Rows shelf, Tableau creates a vertical quantitative axis in the visualization. To construct a combined axis, the associate should drag the second measure directly onto that existing vertical axis, making B correct. A combined axis allows multiple measures to share the same axis instead of displaying them in separate panes or on independent dual axes. When the second measure is dragged onto the existing axis and Tableau displays the appropriate drop indicator, Tableau creates a shared Measure Values-based view containing both measures. The orientation matters. Fields on Rows normally produce a vertical y-axis, whereas fields on Columns normally generate a horizontal x-axis. Consequently, because the first measure was specifically placed on Rows, the target is the vertical axis rather than the horizontal axis. Dropping the second measure on the Marks card would encode that field through a mark property rather than create a combined quantitative axis. Dropping it on Filters would restrict records or values and would not combine the measures. This mechanism should also be distinguished from a dual-axis chart, where two independent axes are layered. A combined axis instead places multiple measures on a shared axis and commonly uses Measure Names and Measure Values for differentiation.
NEW QUESTION # 24
To which type of element can a Tableau associate apply a default aggregation?
Answer: D
Explanation:
A default aggregation can be specified for a measure, making A correct. Measures represent quantitative values and are normally aggregated when they are added to a visualization. For example, a Sales measure typically appears as SUM(Sales). Tableau allows the author to change the measure's default aggregation so that subsequent uses of the field can initially use AVG, MIN, MAX, COUNT, standard deviation, or another supported aggregation instead of SUM. The configuration is available from the Data pane by right-clicking the measure and selecting Default Properties > Aggregation. The choice becomes the default behavior when the field is subsequently added to a view, although the aggregation can still be overridden for an individual instance of the field. Dimensions do not use a default measure aggregation in this manner because their primary role is to partition and categorize the data. Bins are generated categorical fields representing numerical intervals. An already aggregated calculated expression also has restrictions because Tableau cannot arbitrarily aggregate an expression that is already defined at an aggregate level. Tableau's official Default Settings documentation specifically states that users can specify a default aggregation for any measure and documents the Data pane workflow for configuring it.
NEW QUESTION # 25
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