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Tableau TDS-C01 Exam
Tableau Desktop Specialist (Page 12 )

Updated On: 30-Jan-2026
Page 12 of 65
PDF with 317 Questions

Which of the following represent a valid method to create a Bullet Graph with the LEAST number of fields possible?

  1. using 2 measures
  2. using 2 dimensions
  3. using 2 dimensions and 3 measures
  4. using 1 measure

Answer(s): A

Explanation:

A bullet graph is a variation of a bar graph developed to replace dashboard gauges and meters. A bullet graph is useful for comparing the performance of a primary measure to one or more other measures. Below is a single bullet graph showing how actual sales compared to estimated sales.

We can create a Bullet graph with just 2 measures! This method requires the LEAST number of fields possible to create this type of chart.
The best way to tackle such questions in the exam is to click the "SHOW ME" button on top right, and hover over the chart we want to create.

In our case, it is a Bullet graph.





Therefore, we need 2 measures at least to create this chart, and 0 or more dimensions.


Reference:

https://help.tableau.com/current/pro/desktop/en-us/qs_bullet_graphs.htm



Relationships are represented by __________________ and operate at the ____________________.

  1. noodles, logical layer
  2. noodles, physical layer
  3. Venn diagrams, physical layer
  4. Venn diagrams, logical layer

Answer(s): A

Explanation:

From the official documentation:

The default view that you first see in the Data Source page canvas is the logical layer of the data source. You combine data in the logical layer using relationships (or noodles).


Reference:

https://help.tableau.com/current/server/en-us/datasource_datamodel.htm



Our use case states that we need to create a set showing the Bottom 10 products by Profit in each Region.
Which of the following filter types should you apply on Region?

  1. Measure Filters
  2. Context Filters
  3. Extract Filters
  4. Dimension Filters

Answer(s): B

Explanation:

The beauty of context filters is that according to Tableau's Order of Operations, they are executed before Sets.



This means that based on what Region's you've selected - Tableau will first only preserve the rows for those Regions. THEN, after this it will compute the Set , i.e , Bottom 10 products in each Region.

1) First let's create a set to compute the Bottom 10 Products by Profit.



2) Next, take region on the Rows Shelf followed by the Set we just created. Drag Region and the Set to the Filters Shelf as well.



3) Now, try to only visualize the data for the South Region:



4) The problem right now is that Tableau is computing the Set first (Bottom 10 Products), and then applying the Dimension Filter - South Region and hence these values are incorrect. Note how these aren't even 10 products, but rather just 8. To fix this, simply add Region to Context:



Upon doing this, we get the correct answer as :


Reference:

https://help.tableau.com/current/pro/desktop/en-us/order_of_operations.htm https://help.tableau.com/current/pro/desktop/en-us/filtering_context.htm



Most viewers scan content starting at the _____________ of a page.

  1. top left
  2. center
  3. bottom left
  4. bottom right
  5. top right

Answer(s): A

Explanation:

According to Tableau's official documentation:


Reference:

https://help.tableau.com/current/pro/desktop/en-us/dashboards_best_practices.htm



If you decide you want to see all of the marks in the view at the most detailed level of granularity, you can __________________ the view.

  1. sort the measures
  2. disaggregate the measures
  3. break-down the measures
  4. aggregate the measures
  5. split the measures

Answer(s): B

Explanation:



The different aggregations available for a measure determine how the individual values are collected: they can be added (SUM), averaged (AVG), or set to the maximum (MAX) or minimum (MIN) value from the individual row values.
For a complete list of the available aggregations, check out - List of Predefined Aggregations in Tableau.
The level of detail is determined by the dimensions in your view--for information about the concept of level of detail, see How dimensions affect the level of detail in the view. Disaggregating your data can be useful for analyzing measures that you may want to use both independently and dependently in the view. For example, you may be analyzing the results from a product satisfaction survey with the Age of participants along one axis. You can aggregate the Age field to determine the average age of participants or disaggregate the data to determine at what age participants were most satisfied with the product.
Disaggregating data can be useful when you are viewing data as a scatter plot. See Example: Scatter Plots, Aggregation, and Granularity.


Reference:

https://help.tableau.com/current/pro/desktop/en-us/calculations_aggregation.htm



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