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Tableau TCC-C01 Exam Actual Questions
Tableau Certified Consultant (Page 4 )

Updated On: 1-Jul-2026
Page 3 of 16
PDF with 75 Questions

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?

  1. Calc1: DATETRUNC ('month', DATEADD('month', 1, [Order Date])) Calc2: AVG(DATEDIFF ('week', [Order Date], [Calc1]))
  2. Calc1: DATETRUNC ('month', DATEADD ('month', 1, [Order Date])) Calc2: AVG(DATEDIFF ('day', [Order Date], [Calc1]))
  3. Calc1: DATETRUNC ('day', DATEADD('week', 4, [Order Date])) Calc2: AVG([Order Date] - [Calc1])
  4. Calc1: DATETRUNC ('day', DATEADD ('day', 31, [Order Date])) Calc2: AVG ([Order Date] - [Calc1])

Answer(s): B

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.


Reference:

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 documents.

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.



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?

  1. Strings are fastest, followed by integers, and then Booleans.
  2. Integers are fastest, followed by Booleans, and then strings.
  3. Strings, integers, and Booleans all perform the same.
  4. Booleans are fastest, followed by integers, and then strings.

Answer(s): B

Explanation:

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.


Reference:

The performance hierarchy of data types in Tableau calculations is documented in resources that discuss best practices for optimizing Tableau performance.



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?

  1. IF [ProfitMargin]>0.50 Then 'Highly Profitable'
    ELSEIF [ProfitMargin]>=0 Then 'Profitable'
    ELSE 'Unprofitable'
    END
  2. IF [ProfitMargin]>=0.50 Then 'Highly Profitable'
    ELSEIF [ProfitMargin]>=0 Then 'Profitable'
    ELSE 'Unprofitable'
    END
  3. IF [ProfitMargin]>0.50 Then 'Highly Profitable'
    ELSEIF [ProfitMargin]>=0 Then 'Profitable'
    ELSEIF [ProfitMargin] <0 Then 'Unprofitable'
    END
  4. IF([ProfitMargin]>=0.50,'Highly Profitable', 'Profitable') ELSE 'Unprofitable'
    END

Answer(s): B

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%.


Reference:

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.



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?

  1. Split some visualizations on the dashboard into many smaller visualizations on the same dashboard.
  2. Connect to the data source via a custom SQL query.
  3. Use filters, hide unused fields, and aggregate values.
    OD. Restrict users from accessing the workbook to reduce server load.

Answer(s): C

Explanation:

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.


Reference:

The best practices for optimizing workbook performance in Tableau are well-documented in Tableau's official resources, including the Tableau Help Guide and the Designing Efficient Workbooks whitepaper, which provide detailed recommendations on how to streamline workbooks for better performance.



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?

  1. {INCLUDE [Customer ID]: COUNTD([Order ID])}
  2. {FIXED [Customer ID], [Region]: COUNTD([Order ID])}
  3. {EXCLUDE [Customer ID]: COUNTD([Order ID])}
  4. {FIXED [Customer ID], [Region], [Order Date]: COUNTD([Order ID])}

Answer(s): B

Explanation:

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.


Reference:

The LOD expressions in Tableau allow for precise control over the level of detail at which calculations are performed, which is essential for accurate data analysis. The use of {FIXED} expressions to specify the granularity of the calculation is a common practice and is well- documented in Tableau's official resources.

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.



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