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Salesforce Certified Tableau Consultant Sample Questions (Q28-Q33):

NEW QUESTION # 28
A client has a pipeline dashboard that takes a long time to load. The dashboard is connected to only one large data source that is an extract.
It contains two calculated fields:
. TOTAL([Opportunities])
* SUM([Value])
It also contains two filters:
. A Relative Date filter on Created Date, a Date field containing values from 5 years ago until today
. A Multiple Values (Dropdown) filter on Account Name, a String field containing 1,000 distinct values A consultant creates a Performance Recording to troubleshoot the issue, and finds out that the longest-running event is "Executing Query." Which step should the consultant take to resolve this issue?

Answer: A

Explanation:
To improve the loading time of the pipeline dashboard, which primarily suffers from long query execution times due to a comprehensive Relative Date filter:
Relative Date Filter Issue: The existing Relative Date filter on "Created Date" covers a broad range (5 years), leading to significant data processing overhead as it includes granular date calculations over a large dataset.
Optimized Approach: By replacing the Relative Date filter with a Multiple Values (Dropdown) filter based on YEAR([Created Date]), the filter granularity is reduced. Filtering by year simplifies the query by limiting the volume of data processed and reducing the complexity of the filter condition.
Implementation Benefit: This approach still provides the flexibility to view data across different years but does so by reducing the load on the database during query execution, which is critical for improving the performance of the dashboard.
References
This recommendation aligns with Tableau performance optimization strategies, specifically regarding the management of date filters to minimize their impact on query load, as discussed in Tableau performance tuning sessions and guides.


NEW QUESTION # 29

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 # 30
A shipping clerk wants to use a Sankey diagram to analyze the flow of goods between different categories, shipping modes, and locations to spot bottlenecks and optimize the most critical paths. The company uses Tableau Cloud.
How should the shipping clerk create a chart that depicts the above information?

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Tableau Cloud does not natively contain a Sankey diagram in Show Me. Such advanced charts often require:
* A custom extension
* Or specialized templates built into Tableau Exchange
A sandboxed Viz Extension allows users to embed specialized visualization components (like Sankey diagrams) securely in Tableau Cloud. These extensions are designed for advanced chart types that are not available natively.
Accelerators provide prebuilt dashboards but are not intended for custom visual types such as Sankey.
Connectors relate to connecting to data sources, not visualization.
Show Me does not include a Sankey option.
Therefore, downloading a sandboxed Viz Extension is the correct approach.
* Viz Extensions documentation explaining support for custom charts, including Sankey.
* Tableau Exchange listing providing sandboxed visualization extensions for non-native chart types.
* Show Me panel documentation showing Sankey is not an included chart type.


NEW QUESTION # 31
A consultant has a view using a table calculation to calculate percent of total Sales by Category. The consultant would like to filter out particular categories, but wants the percent of total calculation to remain steady even as they filter items in or out.
What should the consultant do to achieve the desired impact?

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The key detail of the question:
"filter out particular categories, but wants the percent of total calculation to remain steady even as they filter items in or out." This means the percent of total must ignore filters.
Table calculations always operate after filters, except table calc filters like "Filter on Table Calculation," and after dimension filters, so filtering categories directly will change the denominator.
Tableau's documented solution for "percent of total that does not change with filtering" is:
# Use a FIXED LOD to define the stable denominator
A FIXED LOD expression "freezes" the aggregation level and is unaffected by dimension filters unless explicitly added to context.
This allows the consultant to compute:
{ FIXED : SUM([Sales]) }
or
{ FIXED [Category] : SUM([Sales]) }
Then percent of total becomes:
SUM([Sales]) / { FIXED : SUM([Sales]) }
The FIXED LOD stores the total before filters are applied, ensuring the percent remains steady.
This is exactly what Tableau documentation explains under:
* Level of Detail Expressions
* LODs and Order of Operations
* Using LODs to create filter-independent calculations
Thus, D is correct.
Why the other answers are wrong:
# A. Context Filter
Context filters run before FIXED LODs but after raw data.
If Category is put into context, LOD totals would be reduced.
Table calculation totals still change because table calcs run near the bottom of the pipeline.
# B. Data Source Filter
Data source filters remove rows before all table calculations and LODs.
This would make the percent of total incorrect, because filtered-out categories would physically be gone.
# C. Aggregate Expression
An aggregate field alone does not solve the issue because it still respects dimension filters.


NEW QUESTION # 32
An online sales company has a table data source that contains Order Date. Products ship on the first day of each month for all orders from the previous month.
The consultant needs to know the average number of days that a customer must wait before a product is shipped.
Which calculation should the consultant use?

Answer: D

Explanation:
The correct calculation to determine the average number of days a customer must wait before a product is shipped is to first find the shipping date, which is the first day of the following month after the order date. This is done using DATETRUNC('month', DATEADD('month', 1, [Order Date])). Then, the average difference in days between the order date and the shipping date is calculated using AVG(DATEDIFF('day', [Order Date], [Calc1])). This approach ensures that the average wait time is calculated in days, which is the most precise measure for this scenario.
References: The solution is based on Tableau's date functions and their use in calculating differences between dates, which are well-documented in Tableau's official learning resources and consultant documents12.
To calculate the average waiting days from order placement to shipping, where shipping occurs on the first day of the following month:
Calculate Shipping Date (Calc1): Use the DATEADD function to add one month to the order date, then apply DATETRUNC to truncate this date to the first day of that month. This represents the shipping date for each order.
Calculate Average Wait Time (Calc2): Use DATEDIFF to calculate the difference in days between the original order date and the calculated shipping date (Calc1). Then, use AVG to average these differences across all orders, giving the average number of days customers wait before their products are shipped.
References:
Date Functions in Tableau: Functions like DATEADD, DATETRUNC, and DATEDIFF are used to manipulate and calculate differences between dates, crucial for creating metrics that depend on time intervals, such as customer wait times in this scenario.


NEW QUESTION # 33
......

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