Analytics-DA-201 Exam Dumps - Salesforce Certified Tableau Data Analyst

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.

To combine the tables by using Tableau Prep and include only values from Table1 that do not match any values in Table2, you should use a left only join. A left only join is a type of join that returns only the rows from the primary table (Table1) that have no matching rows in the secondary table (Table2). The field values from Table2 will appear as null values in the combined table. A left only join is also known as an anti-join or an exclusion join. References:

Tableau Certified Data Analyst Exam Prep Guide , page 9, section “Preparing Data”

Tableau Help: Join Your Data

Tableau Help: Left Only Join

The matching-pattern capability is configured in an Input step in Tableau Prep Builder. When connecting to file-based data such as text files or Excel files, Tableau Prep can include multiple files and create an input union by using wildcard or matching-pattern criteria. This allows similarly named files to be combined into a single table as they are brought into the flow. A separate Union step combines already-added tables or inputs, but the question specifically asks where a single table can be created from a matching pattern of multiple file names. Join combines tables based on key fields, while New Rows generates missing sequential records. Because wildcard file matching is part of the file-ingestion configuration, Input is the correct step.

A suspended data-driven alert can be managed from the My Content area in Tableau’s web interface. Tableau’s current documentation states that when an alert has failed enough times to become suspended, an alert owner can locate the suspended alert in My Content and choose Resume Alert. Tableau now also supports resuming an alert directly from the notification email or from the Alerts panel of the affected view, but neither of those choices appears in this question. Shared with Me manages content shared by other users; Tableau Prep’s notification area does not administer Tableau Server or Cloud data alerts; and Tableau Desktop’s Data Source page is unrelated to server-side alert scheduling. Therefore, among the supplied answers, C remains valid.

To combine the two tables that contain data about books in a library and ensure that all the data is retained, you should use a full outer join. A full outer join is a type of join that returns all rows from both tables, regardless of whether there is a match or not. If there is no match, null values are filled in for the missing fields.

To perform a full outer join, you need to do the following steps:

Connect to both tables as your data sources in Tableau. You can use either live or extract connections.

Drag one table to the canvas and drop it on top of another table. This will create a join between them based on a common field.

Click on the join icon between the tables and select Full Outer Join from the drop-down list. This will change the join type to full outer join and show all rows from both tables.

Optionally, you can add or remove join clauses by clicking on Add or Remove buttons next to each clause. You can also change or rename fields by clicking on them.

The other types of joins are not correct for this scenario. An inner join returns only the rows that have a match in both tables, which will exclude any books that are missing from either table. A left join returns all rows from the left table and only the matching rows from the right table, which will exclude any books that are only in the right table. A right join returns all rows from the right table and only the matching rows from the left table, which will exclude any books that are only in the left table. References: https://help.tableau.com/current/pro/desktop/en-us/joining_tables.htm https://help.tableau.com/current/pro/desktop/en-us/join_types.htm

When combining two datasets that are both incomplete and where it ' s important to retain all data from both sources, a full outer join is appropriate. This type of join ensures that all records from both tables are included in the combined dataset, even if there are no matching records in the other table.