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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Sharing Insights | 25% | - Create and modify a dashboard - Format view for presentation - View and share workbook data |
| Topic 2: Connecting to and Preparing Data | 23% | - Create and manage the data model - Create live connections and extracts - Manage data properties |
| Topic 3: Understanding Tableau Concepts | 15% | - Understand aggregations - Understand discrete and continuous fields - Understand dimensions and measures |
| Topic 4: Exploring and Analyzing Data | 37% | - Organize data and apply filters - Apply analytics to a worksheet - Create basic charts |
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NEW QUESTION # 23
When should a Tableau associate use a relationship instead of a join for two data sets?
Answer: C
Explanation:
A relationship is particularly appropriate when the tables being combined contain data at different levels of detail, making D correct. Tableau relationships operate in the logical layer of the data model and preserve each logical table's native granularity. Tableau determines the necessary joins dynamically during analysis according to the fields used in the visualization. This behavior is materially different from a physical join. A join merges records into a single physical table before analysis. If tables have different granularities, a physical join can duplicate measure values or remove records depending on the join structure and matching keys. Relationships reduce that risk because Tableau aggregates relevant information according to each table's natural level of detail before combining results as required by the visualization. Official Tableau guidance explicitly recommends relationships for combining data from different levels of detail. Relationships also preserve independent logical tables rather than flattening them into a single physical table. Option B merely describes where tables reside and does not determine whether a relationship or join is preferable. Options A and C likewise do not identify the principal modeling scenario addressed by relationships. Relevant Tableau Desktop concepts include logical tables, physical tables, relationships, joins, aggregation behavior, and preservation of level of detail.
NEW QUESTION # 24
From which three locations can a Tableau associate sort a visualization?
Choose three options.
Answer: B,D,E
Explanation:
Tableau provides direct single-click sorting controls from a header, an axis, and a field label, making A, C, and D correct. Tableau's official Sort Data in a Visualization documentation explicitly identifies these three locations. When the user hovers over an appropriate header, numerical axis, or field label, Tableau can display a sort icon. One click typically applies descending sorting, a second click changes it to ascending, and another interaction can clear the sort depending on context. A header is commonly generated by a discrete field and can expose sorting for its members. A numerical axis allows the associated categorical members to be sorted according to measure values. A field label can similarly provide direct sorting when applicable to the view's structure. The Tooltip area of the Marks card controls information displayed when the pointer hovers over a mark; it is not a sorting interface. The Worksheet and Analysis menus contain numerous authoring commands but are not the three direct locations identified by Tableau's single-click sorting workflow in this question.
Official Tableau documentation specifically states: "Quickly sort from an axis, header, or field label," precisely matching A, C, and D.
NEW QUESTION # 25
Which aggregation is available without requiring a table calculation or calculated field?
Answer: D
Explanation:
Standard deviation is available as a native aggregation for a measure and does not require the author to create either a table calculation or a calculated field. Therefore, C is correct. When a measure is used in a Tableau view, its aggregation can be changed through the field menu. In addition to commonly used functions such as SUM, AVG, MIN, MAX, and COUNT, Tableau provides Standard Deviation and Standard Deviation (Population) as built-in aggregation choices. The calculation is performed directly over the records contributing to the current mark. Running Total and Percent of Total are different because they are table calculations. They operate on already aggregated values after Tableau has constructed the visualization and depend on the addressing and partitioning of the view. Sample covariance is supported through calculation functionality such as covariance functions but is not presented as the equivalent direct aggregation option in the basic measure aggregation menu represented by this question. Implementing covariance typically involves specifying the two expressions whose covariance is required. Tableau's official Data Aggregation documentation explicitly lists standard deviation among the aggregations available for measures, including sample and population forms. Consequently, it can be selected directly without constructing an additional calculated field or applying a quick table calculation.
NEW QUESTION # 26
What is a Tableau story point?
Answer: D
Explanation:
A story point is an individual visualization state within a Tableau story, normally represented by a worksheet or dashboard. Therefore, A is correct. Tableau defines a story as a sequence of visualizations that collectively communicate information, provide context, demonstrate relationships, or support a data-driven narrative.
Each individual sheet contained in that sequence is called a story point. A story can contain multiple points, and each point can present a different worksheet, dashboard, filter state, annotation, or analytical stage.
Option C describes the overall story, not an individual story point. The distinction is important: the story represents the complete ordered narrative, while the story point represents one step within that narrative.
Option B is not an official Tableau object or definition. Option D refers to controls within the story workspace and does not define a story point. This functionality allows an analyst to guide viewers through findings in a controlled sequence while preserving interactive visualization capabilities where supported. Official Tableau Desktop documentation explicitly states that a story is a collection of sheets arranged in sequence and that each individual sheet in a story is called a story point.
NEW QUESTION # 27
A Tableau associate has a continuous numeric measure named Sales.
Which chart type is created when the associate double-clicks the Sales measure?
Answer: C
Explanation:
Double-clicking a continuous numeric measure such as Sales in an empty Tableau worksheet automatically produces a bar chart, making C correct. Tableau uses intelligent defaults when fields are double-clicked in the Data pane. With a single measure and no dimensions already defining the view, Tableau aggregates the measure-normally using its default aggregation such as SUM-and adds it to the visualization. The Automatic mark type resolves to a bar, producing a single bar representing the aggregated Sales value.
Tableau's official documentation provides this exact construction using the Profit measure. It states that double-clicking Profit automatically adds the field to the view in an intelligent way, and the documented resulting visualization is a bar chart of Profit. Sales is the same type of field-a quantitative measure-so the same automatic-view rule applies. A line chart ordinarily requires an ordered dimension such as a date field to provide a sequence across which values can be connected. A pie chart requires additional categorical structure for meaningful segmentation. A text table is generally created when a dimension is double-clicked first and subsequent fields refine that cross-tabular structure. Therefore, a lone continuous numeric measure double- clicked in a new worksheet produces the automatic single-measure bar visualization.
NEW QUESTION # 28
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