Tableau TCC-C01 Exam Practice Test Instant Access

Question 1

A client calculates the percent of total sales for a particular region compared to all regions.

Which calculation will fix the automatic recalculation on the % of total field?

A{FIXED [Region]:[Sales]}/{FIXED: SUM([Sales])}

B{FIXED [Region]:sum([Sales])}

C{FIXED [Region]:sum([Sales])}/{FIXED :SUM([Sales])

D{FIXED [Region]:sum([Sales])}/SUM([Sales]}

Answer : D

To correctly calculate the percent of total sales for a particular region compared to all regions, and to ensure that the calculation does not get inadvertently recalculated with each region filter application, the recommended calculation is:

{FIXED [Region]: sum([Sales])}: This part of the formula computes the sum of sales for each region, regardless of any filters applied to the view. It uses a Level of Detail expression to fix the sum of sales to each region, ensuring that filtering by regions won't affect the calculated value.

SUM([Sales]): This part computes the total sum of sales across all regions and is recalculated dynamically based on the filters applied to other parts of the dashboard or worksheet.

Combining the two parts: By dividing the fixed regional sales by the total sales, we get the proportion of sales for each region as compared to the total. This calculation ensures that while the denominator adjusts according to filters, the numerator remains fixed for each region, accurately reflecting the sales percentage without being affected by the region filter directly.

Reference This calculation follows Tableau's best practices for using Level of Detail expressions to manage computation granularity in the presence of dashboard filters, as outlined in the Tableau User Guide and official Tableau training materials.

Question 2

A consultant is designing a dashboard that will be consumed on desktops, tablets, and phones. The consultant needs to implement a dashboard design that

provides the best user experience across all the platforms.

Which approach should the consultant take to achieve these results?

ABuild one dashboard that has desktop, tablet, and phone layouts, and fix the size of the layouts.

BBuild one dashboard and fix the size of the dashboard.

CBuild one dashboard and set the size to Automatic.

DBuild one dashboard for each type of device and fix the size of the layouts.

Answer : A

For a consultant designing a dashboard to be consumed across multiple device types, the best approach is:

Multi-device Layout: Tableau provides the capability to design device-specific layouts within a single dashboard. This feature allows the dashboard to adapt its layout to best fit the screen size and orientation of desktops, tablets, and phones.

Fixed Size Layouts: By fixing the size of each layout, the consultant can ensure that the dashboard appears consistent and maintains the intended design elements and user experience across devices. Fixed sizes prevent components from resizing in ways that could disrupt the dashboard's readability or functionality.

Implementation: In Tableau, you can create these layouts by selecting 'Device Preview' and adding custom layouts for each device type. Here, you define the dimensions and the positioning of sheets and controls tailored to each device's typical viewing mode.

Reference This approach leverages Tableau's device designer capabilities, which are specifically designed to optimize dashboards for multiple viewing environments, ensuring a seamless user experience regardless of the device used. This functionality is well documented in Tableau's official guides on creating and managing device-specific dashboards.

Question 3

An executive-level workbook leverages 37 of the 103 fields included in a data source. Performance for the workbook is noticeably slower than other workbooks

on the same Tableau Server.

What should the consultant do to improve performance of this workbook while following best practice?

ASplit some visualizations on the dashboard into many smaller visualizations on the same dashboard.

BConnect to the data source via a custom SQL query.

CUse filters, hide unused fields, and aggregate values.
OD. Restrict users from accessing the workbook to reduce server load.

Answer : C

To improve the performance of a Tableau workbook, it is best practice to streamline the data being used. This can be achieved by using filters to limit the data to only what is necessary for analysis, hiding fields that are not being used to reduce the complexity of the data model, and aggregating values to simplify the data and reduce the number of rows that need to be processed. These steps can help reduce the load on the server and improve the speed of the workbook.

Question 4

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?

AReplace the Multiple Values (Dropdown) filter with a Multiple Values (Custom List) filter.

BReplace the Relative Date filter with a Multiple Values (Dropdown) filter on YEAR([Created Date]).

CReplace the TOTAL([Opportunities]) calculation with a Grand Total.

DReplace SUM([Value]) with WINDOW_SUM([Value]).

Answer : B

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.

Reference 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.

Question 5

A client's dashboard has two sections dedicated to their shops and warehouses shown when a viewer chooses either shops or warehouses with a parameter.

There are a few quick filters that apply to both, while others apply to only shops or only warehouses.

Currently, the quick filters are all shown at the left side of the dashboard. The client wants to hide all filters, but when shown, make it easy for the viewer to

find the quick filters that work for only shops or only warehouses.

Which solution should the consultant recommend that meets the client's needs and is most user-friendly?

ADivide the quick filters into three groups: General, for shops. Place the general filters on the left of dashboard for warehouses. Place other
filters next to the sections to which they apply.

BUse Dynamic Zone Visibility to inform viewers which quick filters apply to warehouses or shops.

CHide container with all quick filters with a Show/Hide Button.

DUse Dynamic Zone Visibility to show only the quick filters that apply with the chosen parameter value and a Show/Hide Button to hide
container with all the filters.

Answer : D

The most user-friendly solution is to use Dynamic Zone Visibility in combination with a Show/Hide Button. This approach allows the dashboard to dynamically display only the relevant quick filters based on the viewer's selection of shops or warehouses, thus reducing clutter and focusing the user's attention on applicable filters. The Show/Hide Button further enhances the user experience by allowing viewers to toggle the visibility of the filter container, providing a clean and organized dashboard interface1.

Question 6

A client wants to see data for only the last day in a dataset and the last day is always yesterday. The date is represented with the field Ship Date.

The client is not concerned about the daily refresh results. The volume of data is so large that performance is their priority. In the future, the client will be able

to move the calculation to the underlying database, but not at this time.

The solution should offer the best performance.

Which approach should the consultant use to produce the desired results?

AFilter MONTH/DAY/YEAR on [Ship Date] field and use an option to filter to the latest date value when the workbook opens.

BFilter on calculation [Ship Date]=TODAY()-1.

CFilter on Ship Date field using the Yesterday option.

DFilter on calculation [Ship Date]={MAX([Ship Date])}.

Answer : B

The best approach to ensure performance while providing data for only the last day (yesterday) in the dataset is to use a calculated field that filters the data to include only yesterday's date:

Filter on calculation [Ship Date]=TODAY()-1: This calculated field dynamically computes yesterday's date by subtracting one day from today's date. This approach ensures that each day, only the data for the previous day is loaded, which keeps the volume of data minimal and improves performance.

Dynamic Date Calculation: The use of TODAY()-1 ensures the filter remains up-to-date with the changing dates, without the need for manual updates, providing accuracy and timeliness in the dashboard.

This approach is efficient because it avoids the overhead of processing the entire dataset and focuses only on the relevant day's data. It also aligns with Tableau's capabilities for creating dynamic filters using date functions, as highlighted in the Tableau help documentation on date calculations and filters.

Reference This solution utilizes Tableau's built-in date functions and dynamic calculations to optimize performance, as recommended in Tableau's performance optimization resources and date calculation guidelines.

Question 7

A client has a large data set that contains more than 10 million rows.

A consultant wants to calculate a profitability threshold as efficiently as possible. The calculation must classify the profits by using the following specifications:

. Classify profit margins above 50% as Highly Profitable.

. Classify profit margins between 0% and 50% as Profitable.

. Classify profit margins below 0% as Unprofitable.

Which calculation meets these requirements?

AIF [ProfitMargin]>0.50 Then 'Highly Profitable'
ELSEIF [ProfitMargin]>=0 Then 'Profitable'
ELSE 'Unprofitable'
END

BIF [ProfitMargin]>=0.50 Then 'Highly Profitable'
ELSEIF [ProfitMargin]>=0 Then 'Profitable'
ELSE 'Unprofitable'
END

CIF [ProfitMargin]>0.50 Then 'Highly Profitable'
ELSEIF [ProfitMargin]>=0 Then 'Profitable'
ELSEIF [ProfitMargin] <0 Then 'Unprofitable'
END

DIF([ProfitMargin]>=0.50,'Highly Profitable', 'Profitable')
ELSE 'Unprofitable'
END

Answer : B

The correct calculation for classifying profit margins into categories based on specified thresholds involves the use of conditional statements that check ranges in a logical order:

Highly Profitable Classification: The first condition checks if the profit margin is 50% or more. This must use the '>=' operator to include exactly 50% as 'Highly Profitable'.

Profitable Classification: The next condition checks if the profit margin is between 0% and 50%. Since any value falling at or above 50% is already classified, this condition only needs to check for values greater than or equal to 0%.

Unprofitable Classification: The final condition captures any remaining scenarios, which would only be values less than 0%.

Logical Order in Conditional Statements: It is crucial in programming and data calculation to ensure that conditions in IF statements are structured in a logical and non-overlapping manner to accurately categorize all possible values.

Tableau TCC-C01 Tableau Certified Consultant Exam Practice Test