Free Salesforce Analytics-DA-201 Practice Exam with Questions & Answers | Set: 3

Data interpreter is a feature that helps you clean and structure your data in Excel before importing it into Tableau. It detects and removes any headers, footers, subtotals, or other elements that are not part of the actual data. It also splits any merged cells and fills in any missing values. In this case, data interpreter provides the following results:

It removes the first three rows and the last two rows that contain headers and footers.

It splits the merged cells in column A and fills in the missing values with “Fiction” or “Non-Fiction”.

It does not remove or change any other rows or cells.

Therefore, the number of rows of data that will be ingested into Tableau as values is 17, which is the number of rows left after removing the headers and footers. References: https://help.tableau.com/current/pro/desktop/en-us/importing_cleaning_up_data.htm https://help.tableau.com/current/pro/desktop/en-us/importing_data_interpreter.htm

Based on the Data Interpreter ' s results and the provided Excel dataset screenshot, the number of rows of data that will be ingested into Tableau as values is 17. The Data Interpreter has identified and excluded headers and footers, and the rows with actual data are considered for analysis.

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

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.

A Parameter action is designed to let users change a parameter value through direct interaction with a visualization. The parameter can then drive a calculated field that determines which measure—such as Sales, Profit, Quantity, or another metric—is displayed on the chart. Tableau ' s current action framework explicitly includes Change Parameter as an action type and notes that parameter actions can be used with calculations to customize how data is displayed. A Set action changes membership in a set, a Highlight action emphasizes selected marks without changing the metric being displayed, and a Filter action restricts the data shown in the target view. Because the requirement is to let the user dynamically select the measure displayed rather than filter or highlight records, the appropriate action type is Parameter .