RELIABLE TABLEAU TDS-C01 DUMPS QUESTIONS - TDS-C01 EXAM TOPICS PDF

Reliable Tableau TDS-C01 Dumps Questions - TDS-C01 Exam Topics Pdf

Reliable Tableau TDS-C01 Dumps Questions - TDS-C01 Exam Topics Pdf

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Tableau TDS-C01 exam is ideal for individuals who want to enhance their Tableau Desktop skills and showcase their expertise in data visualization. It is particularly useful for data analysts, business analysts, data scientists, and other professionals who work with data on a regular basis. Tableau Desktop Specialist certification provides a competitive edge in the job market and can lead to better career opportunities and higher salaries.

Tableau TDS-C01 Exam consists of multiple-choice questions that cover various topics related to Tableau Desktop, including data connections, data preparation, data analysis, and data visualization. TDS-C01 exam also tests an individual's ability to use Tableau Desktop to create and share dashboards and visualizations. TDS-C01 exam is administered online and can be taken from anywhere in the world.

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Tableau Desktop Specialist Sample Questions (Q175-Q180):

NEW QUESTION # 175
How can you format an axis as Bold in Tableau?

  • A. By choosing the axis and selecting Command/Control + B on your keyboard
  • B. By clicking on Format on the main menu bar, choosing field labels, and setting it to bold.
  • C. By right clicking on the axis, choosing format, and then setting its font to bold.
  • D. By right clicking on the axis, choosing Edit Axis, and then setting its font to bold.

Answer: C

Explanation:
Explanation
To make an axis bold, simply right click it, select format, and then click on Font to choose Bold:

None of the other options are valid ways to make the axis bold.

Read more about editing axis: https://help.tableau.com/current/pro/desktop/en-us/formatting_editaxes.htm


NEW QUESTION # 176
Larger image

What is this view referred to as in Tableau?

  • A. Analytics Pane
  • B. Window Pane
  • C. Dimensions & Measures
  • D. Data Pane

Answer: D

Explanation:
Tableau displays data source connections and data fields for the workbook in the Data pane on the left side of the workspace.
The Data pane includes:
Dimension fields - Fields that contain qualitative values (such as names, dates, or geographical data). You can use dimensions to categorize, segment, and reveal the details in your data. Dimensions affect the level of detail in the view. Examples of dimensions include dates, customer names, and customer segments.
Measure fields - Fields that contain numeric, quantitative values can be measured. You can apply calculations to them and aggregate them. When you drag a measure into the view, Tableau applies an aggregation to that measure (by default). Examples of measures: sales, profit, number of employees, temperature, frequency.
For more information on what dimensions and measures are, see Dimensions and Measures, Blue and Green.
Calculated fields - If your underlying data doesn't include all of the fields you need to answer your questions, you can create new fields in Tableau using calculations and then save them as part of your data source. These fields are called calculated fields.
For more information on calculated fields, see Create Custom Fields with Calculations.
Sets - Subsets of data that you define. Sets are custom fields based on existing dimensions and criteria that you specify. For more information, see Create Sets.
Named sets from an MS Analysis Services server or from a Teradata OLAP connector also appear in Tableau in this area of the Data pane. You can interact with these named sets in the same way you interact with other custom sets in Tableau.
Parameters - Values that can be used as placeholders in formulas, or replace constant values in calculated fields and filters. For more information, see Create Parameters.


NEW QUESTION # 177
What do the colours Blue and Green represent in Tableau?

  • A. Measures and Dimensions
  • B. Dimensions and Measures
  • C. Discrete and Continuous
  • D. Continuous and Discrete

Answer: C

Explanation:
Important question! If you selected Dimension and Measure, don't worry! It is a very common mistake. But we're here to learn aren't we?
When you connect to a new data source, Tableau assigns each field in the data source as dimension or measure in the Data pane, depending on the type of data the field contains. You use these fields to build views of your data.




NEW QUESTION # 178
How can you add color to marks in the view in Tableau?

  • A. Click on Data in the main menu above, and click on choose color.
  • B. From the Data pane, drag a field to Color on the Marks card.
  • C. In the column/row shelf, right click the field and click on edit in shelf to select the color.
  • D. From the Analytics pane, drag a model to Color on the Marks card.

Answer: B

Explanation:
To assign a color to marks in the view, do the following:
From the Data pane, drag a field to Color on the Marks card.
Tableau applies different colors to marks based on the field's values and members. For example, if you drop a discrete field (a blue field), such as Category, on Color, the marks in the view are broken out by category, and each category is assigned a color.

If you drop a continuous field, such as SUM(sales), on Color, each mark in the view is colored based on its sales value.


NEW QUESTION # 179
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.

  • A. split the measures
  • B. disaggregate the measures
  • C. aggregate the measures
  • D. sort the measures
  • E. break-down the measures

Answer: 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.


NEW QUESTION # 180
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