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Test Tableau TCC-C01 Guide - Question TCC-C01 Explanations

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Tableau Certified Consultant Sample Questions (Q55-Q60):

NEW QUESTION # 55
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. Move calculating averages to data layer and aggregate dates to monthly level.
B. Limit data to current and previous years, move calculating averages to data layer, and aggregate dates to monthly level.
C. Limit data to current and previous years and leave data at daily level to calculate the averages in the report.
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 # 56
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?

A. IF([ProfitMargin]>=0.50,'Highly Profitable', 'Profitable')
ELSE 'Unprofitable'
END
B. IF [ProfitMargin]>0.50 Then 'Highly Profitable'
ELSEIF [ProfitMargin]>=0 Then 'Profitable'
ELSEIF [ProfitMargin] <0 Then 'Unprofitable'
END
C. IF [ProfitMargin]>=0.50 Then 'Highly Profitable'
ELSEIF [ProfitMargin]>=0 Then 'Profitable'
ELSE 'Unprofitable'
END
D. IF [ProfitMargin]>0.50 Then 'Highly Profitable'
ELSEIF [ProfitMargin]>=0 Then 'Profitable'
ELSE 'Unprofitable'
END

Answer: C

Explanation:
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%.
References:
* 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.

NEW QUESTION # 57
A client requests a published Tableau data source that is connected to SQL Server. The client needs to leverage the multiple tables option to create an extract. The extract will include partial data from the SQL Server data source.
Which action will reduce the amount of data in the extract?

A. Define the filters by using custom SQL.
B. Set up the extract as an incremental refresh.
C. Aggregate the extract to the visible dimensions.
D. Use an extract filter.

Answer: D

Explanation:
Using an extract filter is an effective way to reduce the amount of data in a Tableau extract. Extract filters allow you to specify a subset of the data to include, which can significantly decrease the size of the extract by excluding unnecessary data. This is particularly useful when you only need partial data from a larger SQL Server data source.
References:The recommendation to use extract filters to reduce data size is supported by Tableau's best practices for optimizing extracts.These practices suggest keeping the extract's data set short through filtering1.Additionally, discussions in the Tableau Community confirm that hiding fields and using extract filters before extracting data can help reduce the extract size2.
When dealing with large datasets in SQL Server and needing to create a manageable extract in Tableau, using an extract filter is the most direct and effective method to limit the data included:
* Extract Filter: This involves setting filters that apply directly when the data is extracted from the source. This means that only the data meeting the specified criteria will be extracted and loaded into Tableau, significantly reducing the size of the extract.
* To apply an extract filter, in the Data Source page in Tableau, drag the fields you want to filter by to the Filters shelf. Then, configure the desired filter criteria. When you create the extract, choose the option to
* "Add Filters to Extract" and select the configured filters. This ensures that only the data that meets these conditions is extracted from the SQL Server.
This approach not only minimizes the data volume but also speeds up performance in Tableau because it processes a smaller subset of the full dataset.
ReferencesThis procedure is described in detail in Tableau's help documentation on managing extracts and optimizing performance by using extract filters, which is recommended for scenarios involving large datasets or when specific subsets of data are required for analysis.

NEW QUESTION # 58
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?

A. Calc1: DATETRUNC ('day', DATEADD('week', 4, [Order Date]))
Calc2: AVG([Order Date] - [Calc1])
B. Calc1: DATETRUNC ('day', DATEADD ('day', 31, [Order Date]))
Calc2: AVG ([Order Date] - [Calc1])
C. Calc1: DATETRUNC ('month', DATEADD ('month', 1, [Order Date]))
Calc2: AVG(DATEDIFF ('day', [Order Date], [Calc1]))
D. Calc1: DATETRUNC ('month', DATEADD('month', 1, [Order Date]))
Calc2: AVG(DATEDIFF ('week', [Order Date], [Calc1]))

Answer: C

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 usingDATETRUNC('month', DATEADD('month', 1, [Order Date])). Then, the average difference in days between the order date and the shipping date is calculated usingAVG(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 # 59
A client wants to provide sales users with the ability to perform the following tasks:
Access published visualizations and published data sources outside the company network.
Edit existing visualizations.
Create new visualizations based on published data sources.
. Minimize licensing costs.
Which site role should the client assign to the sales users?

A. Creator
B. Site Administrator
C. Explorer (can publish)
D. Viewer

Answer: C

Explanation:
The Explorer (can publish) site role in Tableau is designed for users who need to access, edit, and create visualizations based on published data sources, even when they are outside the company network. This role allows users to perform web editing and save their work, making it suitable for sales users who need these capabilities. It is also a cost-effective option as it does not require the full capabilities and associated costs of the Creator license.
References:The information about the Explorer (can publish) role and its capabilities can be found in the official Tableau documentation on site roles and permissions12.This role is appropriate for users who need to interact with published content and create new visualizations without the need for full site administration or advanced content creation tools that come with the Creator role3.

NEW QUESTION # 60
......

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