TABLEAU TCC-C01 EXAM TESTS, LATEST TCC-C01 STUDY PLAN

Tableau TCC-C01 Exam Tests, Latest TCC-C01 Study Plan

Tableau TCC-C01 Exam Tests, Latest TCC-C01 Study Plan

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Tableau Certified Consultant Sample Questions (Q24-Q29):

NEW QUESTION # 24
A client builds a dashboard that presents current and long-term stock measures. Currently, the data is at a daily level. The data presents as a bar chart that presents monthly results over current and previous years. Some measures must present as monthly averages.
What should the consultant recommend to limit the data source for optimal performance?

  • A. Limit data to current and previous years and leave data at daily level to calculate the averages in the report.
  • B. Limit data to current and previous years, move calculating averages to data layer, and aggregate dates to monthly level.
  • C. Move calculating averages to data layer and aggregate dates to monthly level.
  • D. Limit data to current and previous years as well as to the last day of each month to eliminate the need to use the averages.

Answer: B

Explanation:
For optimal performance, it is recommended to limit the data to what is necessary for analysis, which in this case would be the current and previous years. Moving the calculation of averages to the data layer and aggregating the dates to a monthly level will reduce the granularity of the data, thereby improving the performance of the dashboard.This approach aligns with best practices foroptimizing workbook performance in Tableau, which suggest simplifying the data model and reducing the number of records processed12.
References:The recommendation is based on the guidelines provided in Tableau's official documentation on optimizing workbook performance, which includes tips on data management and aggregation for better performance12.


NEW QUESTION # 25
A client wants to count all the distinct orders placed in 2010. They have written the following calculation, but the result is incorrect.
IF YEAR([Date])=2010 THEN COUNTD ([OrderID]) END
Which calculation will produce the correct result?

  • A. IF MIN(YEAR([Date]))=2010 THEN WINDOW_COUNTD([OrderID]) END
  • B. IF YEAR([Date])=2010 THEN {COUNTD ([OrderID])} END
  • C. COUNTD(IF YEAR([Date])=2010 THEN [OrderID] END)
  • D. COUNT(IF YEAR([Date])=2010 THEN [OrderID] END)

Answer: C

Explanation:
The correct calculation to count all distinct orders placed in 2010 involves placing the conditional inside the aggregation function, not the other way around. Here's how to correct the client's calculation:
* Original Calculation Issue: The client's original calculation attempts to apply theCOUNTDfunction within anIFstatement, which does not work as expected because theCOUNTDfunction cannot conditionally count within the scope of theIFstatement.
* Correct Calculation:COUNTD(IF YEAR([Date]) = 2010 THEN [OrderID] END). This calculation checks each order date; if the year is 2010, it returns theOrderID. TheCOUNTDfunction then counts all uniqueOrderIDs that meet this condition.
* Why It Works: This method ensures that each order is first checked for the year condition before being counted, effectively filtering and counting in one step. It efficiently processes the data by focusing the distinct count operation only on relevant records.
ReferencesThis approach is consistent with Tableau's guidance on using conditional logic inside aggregation functions for accurate and efficient data calculations, as detailed in the Tableau User Guide under
"Aggregations and Calculations".


NEW QUESTION # 26
A client builds a dashboard that presents current and long-term stock measures. Currently, the data is at a daily level. The data presents as a bar chart that presents monthly results over current and previous years. Some measures must present as monthly averages.
What should the consultant recommend to limit the data source for optimal performance?

  • A. Limit data to current and previous years and leave data at daily level to calculate the averages in the report.
  • B. Limit data to current and previous years, move calculating averages to data layer, and aggregate dates to monthly level.
  • C. Move calculating averages to data layer and aggregate dates to monthly level.
  • D. Limit data to current and previous years as well as to the last day of each month to eliminate the need to use the averages.

Answer: B


NEW QUESTION # 27
A client wants to flag orders that have sales higher than the regional average.
Which calculated field will produce the required result?

  • A. { FIXED [Order ID] : SUM([Sales]) }
    >
    { FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) }
  • B. { FIXED [Order ID] : SUM([Sales]) }
    >
    { FIXED [Region] : SUM([Sales]) }
  • C. [Sales]
    >
    { FIXED [Order ID] : SUM([Sales]) }
  • D. { FIXED [Order ID] : SUM([Sales]) }
    >
    { INCLUDE [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) }

Answer: A

Explanation:
To flag orders with sales higher than the regional average, the correct calculated field would compare the sum of sales for each order against the average sales of all orders within the same region:
* Correct Formula:{ FIXED [Order ID] : SUM([Sales]) } > { FIXED [Region] : AVG({ FIXED
[Order ID] : SUM([Sales]) }) }
* This calculation uses a Level of Detail (LOD) expression:
* The left part of the formula{ FIXED [Order ID] : SUM([Sales]) }calculates the total sales for each individual order.
* The right part{ FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) }calculates the average sales per order within each region.
* The>operator is used to compare these two values to determine if the sales for each order exceed the regional average.
ReferencesThis formula utilizes Tableau's LOD expressions to perform complex comparisons across different dimensions of the data, as explained in Tableau's official training materials on LOD calculations.


NEW QUESTION # 28

From the desktop, open the NYC
Property Transactions workbook.
You need to record the performance of
the Property Transactions dashboard in
the NYC Property Transactions.twbx
workbook. Ensure that you start the
recording as soon as you open the
workbook. Open the Property
Transactions dashboard, reset the filters
on the dashboard to show all values, and
stop the recording. Save the recording in
C:CCData.
Create a new worksheet in the
performance recording. In the worksheet,
create a bar chart to show the elapsed
time of each command name by
worksheet, to show how each sheet in
the Property Transactions dashboard
contributes to the overall load time.
From the File menu in Tableau Desktop,
click Save. Save the performance
recording in C:CCData.

Answer:

Explanation:
See the complete Steps below in Explanation:
Explanation:
To record the performance of the Property Transactions dashboard in the NYC Property Transactions.twbx workbook and analyze it using a bar chart, follow these detailed steps:
* Open the NYC Property Transactions Workbook:
* From the desktop, double-click the NYC Property Transactions.twbx workbook to open it in Tableau Desktop.
* Start Performance Recording:
* Before doing anything else, navigate to the 'Help' menu in Tableau Desktop.
* Select 'Settings and Performance', then choose 'Start Performance Recording'.
* Open the Property Transactions Dashboard and Reset Filters:
* Navigate to the Property Transactions dashboard within the workbook.
* Reset all filters to show all values. This usually involves selecting the dropdown on each filter and choosing 'All' or using a 'Reset' button if available.
* Stop the Performance Recording:
* Go back to the 'Help' menu.
* Choose 'Settings and Performance', then select 'Stop Performance Recording'.
* Tableau will automatically open a new tab displaying the performance recording results.
* Save the Performance Recording:
* In the performance recording results tab, go to the 'File' menu.
* Click 'Save As' and navigate to the C:CCData directory.
* Save the file, ensuring it is stored in the desired location.
* Create a New Worksheet for Performance Analysis:
* Return to the NYC Property Transactions workbook and create a new worksheet by clicking on the 'New Worksheet' icon.
* Drag the 'Command Name' field to the Columns shelf.
* Drag the 'Elapsed Time' field to the Rows shelf.
* Ensure that the 'Worksheet' field is also included in the analysis to break down the time by individual sheets within the dashboard.
* Choose 'Bar Chart' from the 'Show Me' options to display the data as a bar chart.
* Customize and Finalize the Bar Chart:
* Adjust the axes and labels to clearly display the information.
* Format the chart to enhance readability, applying color coding or sorting as needed to emphasize sheets with longer load times.
* Save Your Work:
* Once the new worksheet and the performance recording are complete, ensure all work is saved.
* Navigate to the 'File' menu and click 'Save', confirming that changes are stored in the workbook.
References:
* Tableau Help Documentation: Provides guidance on how to start and stop performance recordings and analyze them.
* Tableau Visualization Techniques: Offers tips on creating effective bar charts for performance data.
By following these steps, you have successfully recorded and analyzed the performance of the Property Transactions dashboard, providing valuable insights into how each component of the dashboard contributes to the overall load time. This analysis is crucial for optimizing dashboard performance and ensuring efficient data visualization.


NEW QUESTION # 29
......

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