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Salesforce Analytics-DA-201 Exam Syllabus Topics:

Section	Weight	Objectives
Create Content	26%	- Create stories 1. Combine visualizations and text 2. Sequence and present insights effectively - Design visualizations 1. Formatting, colors, annotations, and tooltips 2. Select appropriate chart types and visual best practices - Build interactive dashboards 1. Responsive design and performance 2. Actions, filters, and interactivity features 3. Layout and design principles
Connect to and Transform Data	24%	- Transform data with Tableau Prep 1. Build flows, apply cleaning steps 2. Output and publish data sources - Prepare and clean data 1. Shape, filter, and aggregate data 2. Handle null values, duplicates, and inconsistencies 3. Use joins, unions, relationships, and blends - Connect to various data sources 1. Choose between live connection and extract 2. File-based data, databases, cloud sources, published data sources
Publish and Manage Content on Tableau Server and Tableau Cloud	9%	- Publish content 1. Publish workbooks, data sources, and flows 2. Set permissions and access rights - Manage and maintain content 1. Monitor usage and optimize performance 2. Schedule refreshes and subscriptions 3. Create alerts, metrics, and custom views
Explore and Analyze Data	41%	- Analyze and interpret data 1. Statistical functions, forecasting, and clustering 2. Trends, distributions, comparisons, and correlations 3. Geospatial analysis - Organize and simplify data 1. Apply filters and sets 2. Sort, group, bin, and create hierarchies - Create calculations and apply logic 1. Parameters, conditional logic, and logical functions 2. Basic calculations, table calculations, LOD expressions

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Salesforce Certified Tableau Data Analyst Analytics-DA-201 Prüfungsfragen mit Lösungen (Q132-Q137):

132. Frage
You have the following map.

You need the map to appear as shown in the following visualization.

What should you do?

A. Change the mark type to Density
B. Drag Location to Size on the Marks card
C. Change the opacity to 75%
D. Drag Population to Size on the Marks card
E. Change the mark type to Map

Antwort: A

Begründung:
The question presents a scenario where a geographic map visualization in Tableau needs to be transformed from a series of discrete circles representing data points to a density map visualization. The density map shows concentrations of data points with a gradient of color, where denser areas are indicated by a darker color.
Here's the explanation for each option:
A). Change the mark type to Density: This is the correct answer because changing the mark type to 'Density' in Tableau creates a density map, which displays the concentration of data points with a color gradient. This is exactly what is needed to achieve the visual effect shown in the second image, where regions with a higher concentration of data points are represented by darker shades.
B). Drag Location to Size on the Marks card: This option would adjust the size of the marks based on the number of locations, which is not relevant to creating a density map. It would result in varying sizes of circles, not a continuous gradient.
C). Change the mark type to Map: The visualization is already using a map. This option would not change the visualization to the desired density map.
D). Drag Population to Size on the Marks card: This would change the size of the circles based on the population values, making some circles larger and others smaller. This is not how a density map is created, which uses color intensity rather than size to show concentration.
E). Change the opacity to 75%: Changing the opacity would affect the transparency of the marks on the map but would not transform the visualization into a density map.
To achieve the visualization shown in the second image, the mark type must be changed to 'Density,' which will produce a heat map-like effect where the color intensity represents the concentration of data points.
Therefore, the correct answer is A. Change the mark type to Density.
To create a density map from a scatter plot of data points, you would change the mark type to Density. This mark type allows you to visualize the concentration of data points in an area, which can be useful for identifying clusters or patterns in geospatial data.

133. Frage
You have the following two datasets:
* A Microsoft Excel worksheet that has two columns named Employee Name and Department
* A Microsoft SQL Server table that has three columns named Employee Name. Pay Grade and Team Size.
You want to use Tableau Prep to join the two datasets.
Which three actions should you perform in order? (Place the three correct options in order.)

Antwort:

Begründung:
The correct order of the three actions is:
* From the connections pane, connect to both data sources
* Join both datasets and select the join type
* Specify Employee Name as the join key
The first action is to connect to both data sources from the connections pane in Tableau Prep. The connections pane is where you can access and add data sources to your flow. You can connect to various types of data sources, such as Excel, SQL Server, or Tableau Server. In this case, you want to connect to an Excel worksheet and a SQL Server table.
The second action is to join both datasets and select the join type. A join is a way of combining data from two or more tables based on a common field. You can join datasets by dragging one table to the canvas and dropping it on top of another table. This will create a join step in your flow. You can select the join type from the drop-down list on the join step. The join type determines which rows are returned from the tables.
The third action is to specify Employee Name as the join key. A join key is a field that is used to match rows from different tables. You can specify the join key by clicking on the field name in each table and dragging it to the center of the join step. This will create a join clause that shows the field name and the operator. In this case, you want to use Employee Name as the join key, because it is a common field between the two datasets.
The other options are not relevant for this scenario. Specifying Department and Team Size as a join condition will not work, because they are not common fields between the two datasets. Opening both data sources in Tableau Desktop will not help you join them in Tableau Prep. Adding a step to aggregate the data in the SQL Server table will not affect the join, but it may change the level of detail of your data. References: https://help.
tableau.com/current/prep/en-us/prep_connect.htm https://help.tableau.com/current/prep/en-us/prep_join.htm
https://help.tableau.com/current/prep/en-us/prep_join_types.htm

134. Frage
A Data Analyst has the following worksheet in a dashboard that has a hierarchy from Market to Market Size in the view.
A stakeholder wants to see the total sales for each Market/Market Size depending on which hierarchy level is in the view.
Which calculation should the analyst use to get the Market and/or Market Size total?

A. { FIXED [State] : SUM([Sales] ) }
B. { EXCLUDE [State] : TOTAL([Sales] ) }
C. { FIXED [Market] : { FIXED [Market Size] : SUM([Sales]) } }
D. { EXCLUDE [State] : SUM([Sales] ) }

Antwort: D

Begründung:
The hierarchy contains Market, Market Size, and State, but the stakeholder wants the total at whichever higher hierarchy level remains in the view. An EXCLUDE LOD expression is designed for exactly this type of dynamic view-dependent aggregation because it removes a specified dimension from the current visualization level of detail while retaining the other dimensions that are present. Using {EXCLUDE [State] :
SUM([Sales] )} removes State from the calculation. If Market and Market Size are visible, the result is calculated at that combination; if the hierarchy is collapsed so only Market remains, the calculation adapts to Market. A FIXED expression would establish a fixed granularity independent of the view and would not adapt in the same way. Therefore, option A provides the required hierarchy-sensitive total.

135. Frage
What is a key benefit of using parameter actions in Tableau?

A. They restrict user interaction to predefined filters.
B. They allow users to dynamically change calculated fields based on selections.
C. They create a static view of the data without any interactivity.
D. They allow users to turn parameters into filters on the dashboard.

Antwort: B

Begründung:
Parameter actions allow a user to change a parameter value by directly interacting with marks in a visualization, such as by selecting or hovering over a mark. When that parameter is referenced by a calculated field, changing the parameter immediately changes the calculation ' s result and therefore the information displayed in the visualization. This enables highly interactive experiences without requiring the user to manipulate a separate parameter control. Tableau also supports parameter actions with reference lines, filters, and SQL queries. The action does not literally convert a parameter into a filter, nor does it create a static view or restrict interaction to predefined filters. Therefore, D best represents the advantage tested here: selections in the visualization can dynamically update parameter-driven calculations and consequently change the displayed analysis.

136. Frage
You have a data source that contains the following columns.

You want to sort customers based on the most expensive orders.
How should you complete the formula? (Use the dropdowns in the Answer Area to select the correct options to complete the formula.)

Antwort:

Begründung:

137. Frage
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