Tableau TCC-C01 Exam Practice Test Instant Access

Question 1

From the desktop, open the CC workbook. Use the US Population Estimates data source.

You need to shape the data in US Population Estimates by using Tableau Desktop. The data must be formatted as shown in the following table.

Open the Population worksheet. Enter the total number of records contained in the data set into the Total Records parameter.

From the File menu in Tableau Desktop, click Save.

ASee the complete Steps below in Explanation

Answer : A

To shape the data in the 'US Population Estimates' data source and enter the total number of records into the 'Total Records' parameter in Tableau Desktop, follow these steps:

Open the CC Workbook and Access the Worksheet:

From the desktop, double-click on the CC workbook to open it in Tableau Desktop.

Navigate to the Population worksheet by selecting its tab at the bottom of the window.

Format and Shape the Data:

Ensure the data types match those specified in the requirements: Sex, Origin, Race as strings; Year, Age, Population as whole numbers.

To verify or change the data type, click on the dropdown arrow next to each field name in the Data pane and select 'Change Data Type' if necessary.

Calculate Total Number of Records:

Create a new calculated field named 'Total Records'. To do this, right-click in the Data pane and select 'Create Calculated Field'.

Enter the formula COUNT([Record ID]) or SUM([Number of Records]) depending on how the data source identifies each row uniquely.

Drag this new calculated field onto the worksheet to display the total number of records.

Enter the Value into the Total Records Parameter:

Locate the 'Total Records' parameter in the Data pane. Right-click on the parameter and select 'Edit'.

Manually enter the number displayed from the calculated field into the parameter, ensuring accuracy to meet the data shaping requirement.

Save Your Changes:

From the File menu, click 'Save' to ensure all your changes are stored.

Tableau Desktop Guide: Provides detailed instructions on managing data types, creating calculated fields, and updating parameters.

Tableau Data Shaping Techniques: Outlines effective methods for manipulating and structuring data for analysis.

This process will ensure the data in the 'US Population Estimates' is accurately shaped according to the specified format and that the total number of records is correctly calculated and entered into the designated parameter. This thorough approach ensures data integrity and accuracy in reporting.

Question 2

For a new report, a consultant needs to build a data model with three different tables, including two that contain hierarchies of locations and products. The third

table contains detailed warehousing data from all locations across six countries. The consultant uses Tableau Cloud and the size of the third table excludes

using an extract.

What is the most performant approach to model the data for a live connection?

ARelating the tables in Tableau Desktop

BBlending the first two tables with the third

CJoining the tables in Tableau Prep

DJoining the tables in Tableau Desktop

Answer : A

For a performant live connection in Tableau Cloud, especially when dealing with large datasets that preclude the use of extracts, relating the tables in Tableau Desktop is the recommended approach. This method allows for flexibility in how the data is queried and can improve performance by leveraging Tableau's relationships feature, which optimizes queries for the underlying database.

Question 3

A consultant builds a report where profit margin is calculated as SUM([Profit]) / SUM([Sales]). Three groups of users are organized on Tableau Server with the

following levels of data access that they can be granted.

. Group 1: Viewers who cannot see any information on profitability

. Group 2: Viewers who can see profit and profit margin

. Group 3: Viewers who can see profit margin but not the value of profit

Which approach should the consultant use to provide the required level of access?

AUse user filters to access data on profitability to all groups. Then, create a calculated field that allows visibility of profit value to Group 2 and
use the calculation in the view in the report.

BSpecify in the row-level security (RLS) entitlement table individuals who can see profit, profit margin, or none of these. Then, use the table
data to create user filters in the report.

CUse user filters to allow only Groups 2 and 3 access to data on profitability. Then, create a calculated field that limits visibility of profit value to
Group 2 and use the calculation in the view in the report.

DSpecify with user filters in each view individuals who can see profit, profit margin, or none of these.

Answer : C

The approach of using user filters to control access to data on profitability for Groups 2 and 3, combined with a calculated field that restricts the visibility of profit value to only Group 2, aligns with Tableau's best practices for managing content permissions. This method ensures that each group sees only the data they are permitted to view, with Group 1 not seeing any profitability information, Group 2 seeing both profit and profit margin, and Group 3 seeing only the profit margin without the actual profit values. This setup can be achieved through Tableau Server's permission capabilities, which allow for detailed control over what each user or group can see and interact with12.

Question 4

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?

ALimit data to current and previous years and leave data at daily level to calculate the averages in the report.

BLimit data to current and previous years, move calculating averages to data layer, and aggregate dates to monthly level.

CMove calculating averages to data layer and aggregate dates to monthly level.

DLimit 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

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 for optimizing workbook performance in Tableau, which suggest simplifying the data model and reducing the number of records processed12.

Question 5

A client wants to see the average number of orders per customer per month, broken down by region. The client has created the following calculated field:

Orders per Customer: {FIXED [Customer ID]: COUNTD([Order ID])}

The client then creates a line chart that plots AVG(Orders per Customer) over MONTH(Order Date) by Region. The numbers shown by this chart are far higher

than the customer expects.

The client asks a consultant to rewrite the calculation so the result meets their expectation.

Which calculation should the consultant use?

A{INCLUDE [Customer ID]: COUNTD([Order ID])}

B{FIXED [Customer ID], [Region]: COUNTD([Order ID])}

C{EXCLUDE [Customer ID]: COUNTD([Order ID])}

D{FIXED [Customer ID], [Region], [Order Date]: COUNTD([Order ID])}

Answer : B

The calculation {FIXED [Customer ID], [Region]: COUNTD([Order ID])} is the correct one to use for this scenario. This Level of Detail (LOD) expression will calculate the distinct count of orders for each customer within each region, which is then averaged per month. This approach ensures that the average number of orders per customer is accurately calculated for each region and then broken down by month, aligning with the client's expectations.

The initial calculation provided by the client likely overestimates the average number of orders per customer per month by region due to improper granularity control. The revised calculation must take into account both the customer and the region to correctly aggregate the data:

FIXED Level of Detail Expression: This calculation uses a FIXED expression to count distinct order IDs for each customer within each region. This ensures that the count of orders is correctly grouped by both customer ID and region, addressing potential duplication or misaggregation issues.

Accurate Aggregation: By specifying both [Customer ID] and [Region] in the FIXED expression, the calculation prevents the overcounting of orders that may appear if only customer ID was considered, especially when a customer could be ordering from multiple regions.

Level of Detail Expressions in Tableau: These expressions allow you to specify the level of granularity you need for your calculations, independent of the visualization's level of detail, thus offering precise control over data aggregation.

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