Tableau TCC-C01 Exam Practice Test Instant Access

Question 1

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?

AIF MIN(YEAR([Date]))=2010 THEN WINDOW_COUNTD([OrderID]) END

BIF YEAR([Date])=2010 THEN {COUNTD ([OrderID])} END

CCOUNTD(IF YEAR([Date])=2010 THEN [OrderID] END)

DCOUNT(IF YEAR([Date])=2010 THEN [OrderID] END)

Answer : C

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 the COUNTD function within an IF statement, which does not work as expected because the COUNTD function cannot conditionally count within the scope of the IF statement.

Correct Calculation: COUNTD(IF YEAR([Date]) = 2010 THEN [OrderID] END). This calculation checks each order date; if the year is 2010, it returns the OrderID. The COUNTD function then counts all unique OrderIDs 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.

Reference This 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'.

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

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 4

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 5

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 6

A client notices that while creating calculated fields, occasionally the new fields are created as strings, integers, or Booleans. The client asks a consultant if

there is a performance difference among these three data types.

What should the consultant tell the customer?

AStrings are fastest, followed by integers, and then Booleans.

BIntegers are fastest, followed by Booleans, and then strings.

CStrings, integers, and Booleans all perform the same.

DBooleans are fastest, followed by integers, and then strings.

Answer : B

In Tableau, the performance of calculated fields can vary based on the data type used. Calculations involving integers and Booleans are generally faster than those involving strings. This is because numerical operations are typically more efficient for a computer to process than string operations, which can be more complex and time-consuming. Therefore, when performance is a consideration, it is advisable to use integers or Booleans over strings whenever possible.

Question 7

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?

ACalc1: DATETRUNC ('month', DATEADD('month', 1, [Order Date]))
Calc2: AVG(DATEDIFF ('week', [Order Date], [Calc1]))

BCalc1: DATETRUNC ('month', DATEADD ('month', 1, [Order Date]))
Calc2: AVG(DATEDIFF ('day', [Order Date], [Calc1]))

CCalc1: DATETRUNC ('day', DATEADD('week', 4, [Order Date]))
Calc2: AVG([Order Date] - [Calc1])

DCalc1: DATETRUNC ('day', DATEADD ('day', 31, [Order Date]))
Calc2: AVG ([Order Date] - [Calc1])

Answer : B

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.

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.

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.

Tableau TCC-C01 Tableau Certified Consultant Exam Practice Test