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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Sharing Insights | 25% | - Ensure accessibility and clarity - Publish and share workbooks - Export and publish visualizations - Set permissions and access |
| Topic 2: Connecting to and Preparing Data | 23% | - Connect to various data sources - Manage data fields and hierarchies - Join and union data - Data cleaning and transformation |
| Topic 3: Exploring and Analyzing Data | 37% | - Create interactive dashboards and stories - Sort, group, and summarize data - Create and customize visualizations - Apply calculations and aggregations - Use filters, parameters, and sets |
| Topic 4: Understanding Tableau Concepts | 15% | - Workbook, worksheet, and dashboard structure - Visualization best practices - Dimensions vs measures - Data types and roles |
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NEW QUESTION # 15
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: D
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 # 16
Which three elements are included in a packaged workbook (.twbx)?
Answer: B,C,D
Explanation:
A Tableau packaged workbook uses the .twbx format to bundle a workbook with supporting local resources needed to use it outside the author's original environment. The correct elements are background images, custom shapes, and extract files. Tableau explicitly lists background images, custom shapes, and Tableau extract files (.hyper) among the resources that can be incorporated into a packaged workbook. These supporting assets are bundled so that another user does not need access to the original local paths used by the workbook author. Other packageable resources include custom geocoding, local cube files, Excel files, Access files, and text-based local data sources. A PDF version of the workbook is not automatically generated and embedded inside a .twbx, making C incorrect. A Tableau Datasource Customization .tdc file is likewise not listed among the standard resources incorporated into a packaged workbook. A .twbx is therefore fundamentally a portable Tableau workbook package rather than simply a workbook definition. This distinction is important when distributing workbooks that depend on local resources. Official Tableau Desktop documentation lists background images, custom shapes, and Tableau extract files among the files included in packaged workbooks.
NEW QUESTION # 17
When should a Tableau associate use a relationship instead of a join for two data sets?
Answer: B
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 # 18
Which two elements can have their values changed by using a dashboard action?
Answer: B,C
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 # 19
Which aggregation is available without requiring a table calculation or calculated field?
Answer: B
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 # 20
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