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Tableau TDS-C01 certification exam consists of 36 multiple-choice questions that are designed to test the candidate's knowledge of Tableau Desktop. TDS-C01 exam covers a range of topics, including connecting to data, creating basic charts and graphs, creating calculated fields and groups, and sharing insights with others. TDS-C01 Exam is timed and candidates have 120 minutes to complete the exam. Candidates who pass the certification exam will receive a Tableau Desktop Specialist certificate, which demonstrates their proficiency in using Tableau Desktop to analyze and visualize data.
NEW QUESTION # 22
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What is this view referred to as in Tableau?
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 # 23
Which of the following are true about dimensions?
Answer: A,B,D
Explanation:
Measures contain numeric quantitative values hence that option is incorrect.
Reference 1: https://help.tableau.com/current/pro/desktop/en-us/datafields_typesandroles.htm Reference 2: https://help.tableau.com/current/pro/desktop/en-us/dates.htm
NEW QUESTION # 24
Which of the following is a good reason for using a bullet graph?
Answer: D
Explanation:
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.
NEW QUESTION # 25
Yes or No: The number of marks will increase when you increase the number of Dimensions in a view
Answer: B
Explanation:
Of course! As an example, see below:
1) Using the Sample Superstore data, let's plot a bar chart showing the Sales for each Category:
Observe that we have 3 marks - Each bar in a bar chart is called a mark. Similarly, each point in a scatter plot is also a mark , and so on for all charts.
1 row by 3 columns means that clearly on the y-axis (Sales), we have only a single mark - a single continous axis, but 3 different marks (Technology, Furniture and Office supplies ) on the x-axis.
2) Now let's add subcategory to the view as well (another dimension):
Observe that the number of marks has increased - i.e the number of Bars.
Also, notice we now have 1 row and 17 columns. Simply because 1 row = Sales (on the y-axis), and on the x-axis, we have 17 different columns (i.e product sub categories!!)
NEW QUESTION # 26
Which of the following would you use to connect to multiple tables in a single data source at once?
Answer: A
Explanation:
The data that you analyze in Tableau is often made up of a collection of tables that are related by specific fields (that is, columns). Joining is a method for combining data on based on those common fields. The result of combining data using a join is a virtual table that is typically extended horizontally by adding columns of data.
For example, consider the following two tables originating from a single data source:
We can combine these 2 tables, simply by joining the tables on ID to answer questions like, "How much was paid in royalties for authors from a given publisher?". By combining tables using a join, you can view and use related data from different tables in your analysis.
NEW QUESTION # 27
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