The simulation requires performing a series of actions where the final step, entering the total record count, depends on the specific data file. The correct procedure is as follows:

1. On the Data Source page, verify the data types. Click the icon next to each field name and select the correct type as specified in the image (e.g., String for Sex, Number (whole) for Year).
2. Navigate to the Population worksheet.
3. In the Data pane, locate the measure named US Population Estimates(Count).
4. Drag this US Population Estimates(Count) field onto the Text mark. The view will update to show a single number, which is the total record count.
5. In the Data pane, right-click the Total Records parameter and select Show Parameter.
6. Enter the number from step 4 into the Total Records parameter card that appears in the view.
7. Click File > Save.

Tableau Help (Official Documentation). Data Types.

Section: "Change the data type for a field in the Data Source page"

Relevance: Confirms the procedure for Step 1, which is to click the data type icon on the Data Source page grid and select the correct type (e.g., "String", "Number (whole)") from the drop-down menu.

Tableau Help (Official Documentation). Relate Your Data.

Section: "Options for validating relationships and the data model"

Relevance: Confirms the procedure for Step 3 and 4. It states: "Every table includes a count of its records, as a field named TableName(Count)... To see the count for a table, drag its Count field into the view."

Tableau Help (Official Documentation). Create Parameters.

Section: "Show a parameter control in the viz"

Relevance: Confirms the procedure for Step 5 and 6. To show the parameter control card, "right-click (Control-click) the parameter in the Data pane and select Show Parameter." The value can then be entered into this card.

This procedure correctly configures the Pareto chart. First, a dual-axis chart is created to overlay a line graph (for cumulative percentage) on the bar chart (for individual sales). Table calculations are then applied to both the sales measure and the city dimension to compute the running total as a percentage of the total. This creates the characteristic Pareto curve and the cumulative percentage axis. Finally, constant reference lines are added at 80% on the sales axis and 20% on the city axis to visually represent the Pareto principle (the 80/20 rule), which is the core requirement of the task.

1. Tableau Desktop and Web Authoring Help: Create a Pareto Chart. This official documentation outlines the step-by-step process for building a Pareto chart

including creating a dual-axis chart

adding a running total table calculation

and then adding a secondary percent of total calculation. The steps provided in the answer directly follow this documented procedure.

2. Tableau Desktop and Web Authoring Help: Reference Lines

Bands

Distributions

and Boxes. This document details how to add reference lines from the Analytics pane. Specifically

it covers adding a line for a constant value to an axis

as required by the prompt to show the 80% and 20% marks for the Pareto principle. (See section: "Add a Reference Line").

The goal is to calculate each manufacturer's contribution to the total quantity.

1. SUM([Quantity]): This aggregation calculates the quantity for the level of detail in the view (i.e., for each manufacturer).
2. {FIXED : SUM([Quantity])}: This FIXED LOD expression calculates the grand total quantity across the entire dataset, independent of the dimensions or filters* in the view.
3. Dividing the manufacturer's sum by this fixed grand total provides the correct percentage contribution.

After applying this formula, you would identify the value for "Newell" and enter it into the specified parameter.

(*Note: FIXED LOD expressions are computed before dimension filters. If filters are present and should apply to this calculation, they must be added to context.)

Tableau (Official Vendor Documentation). (2024). Create Level of Detail Expressions in Tableau. Tableau Help.

Section: "FIXED"

Relevance: This document explains that FIXED level of detail expressions "compute values using the specified dimensions, without reference to the dimensions in the view." Using {FIXED : SUM([Quantity])} without any specified dimensions computes the total sum for the entire context, which is required for the denominator.

Tableau (Official Vendor Documentation). (2024). Overview: Level of Detail Expressions. Tableau Help.

Section: "Aggregation and Level of Detail Expressions"

Relevance: This document clarifies how Tableau aggregates the results of an LOD expression. In the formula SUM([Quantity]) / SUM({FIXED : SUM([Quantity])}), the denominator is aggregated as SUM() because the LOD expression itself returns a single, non-aggregated value (the grand total) that is duplicated for every row.

Tableau (Official Vendor Documentation). (2024). Top 15 LOD Expressions (Whitepaper).

Section: Example 2, "Percent of Total"

Relevance: This official whitepaper provides a canonical example of using an LOD expression for a "Percent of Total" calculation, often using the formula {FIXED [Dimension] : SUM([Sales])} / {FIXED : SUM([Sales])}. The formula provided in the answer, SUM([Quantity]) / SUM({FIXED : SUM([Quantity])}), is an equivalent and efficient way to achieve this when [Manufacturer] is already in the view.

This task requires a nested table calculation (also known as a secondary table calculation) to satisfy both requirements.

1. The primary calculation addresses the first bullet: "calculate the average sales by segment." By setting the Calculation Type to Average and computing using Segment, Tableau first averages the sales figures for the Consumer, Corporate, and Home Office segments for each Category/Year intersection.
2. The secondary calculation addresses the second bullet: "compute the year-over-year percentage change." By adding a secondary calculation of Percent Difference From and computing using YEAR(Order Date), Tableau takes the result of the first calculation (the average) and computes its percentage change from the previous year.

Tableau (Salesforce) Official Documentation. Tableau Desktop and Web Authoring Help. "Add a secondary table calculation".

Reference: This document explicitly details the procedure for applying a secondary calculation to a measure that already has a primary table calculation. It states, "You can add a secondary table calculation to a measure that already has a table calculation applied to it... For example, you can apply a primary table calculation to calculate the yearly running total of sales, and then apply a secondary table calculation to calculate the year-over-year percent difference."

Tableau (Salesforce) Official Documentation. Tableau Desktop and Web Authoring Help. "Table Calculation Types".

Reference: Section: Percent Difference From. This reference confirms that Percent Difference From is the correct calculation type for this scenario, as it "computes the percent difference from the previous value in the partition."

Tableau (Salesforce) Official Documentation. Tableau Desktop and Web Authoring Help. "Transform Values with Table Calculations".

Reference: Section: Compute Using. This guide explains the necessity of using Specific Dimensions to control the scope and direction of the calculation. For the primary calculation, the scope is Segment; for the secondary, the scope is YEAR(Order Date).

MIT Libraries. Data Management and Visualization Guide. "Tableau: Table Calculations".

Reference: This university courseware guide confirms the nature of table calculations, stating they "are computed based on what is currently in the visualization" and can be used for "percent of total... [and] percent difference". It reinforces the use of these functions on the data present in the crosstab.

The Split feature in Tableau Prep is the most efficient tool for this task. The Orderld field has a consistent structure where components (Country, Year, Order Number) are separated by a hyphen (-). The Split function can use this delimiter to automatically parse the single field into three distinct fields in one operation. This built-in functionality is significantly faster and requires fewer steps than manually creating multiple calculated fields with complex string functions, directly addressing the need for the "most efficient manner."

A. A calculated field that uses the TRIM function: The TRIM function is incorrect as it only removes leading and trailing spaces from a string; it cannot separate the string into multiple parts.

B. A calculated field that uses the LEFT function: While possible, using LEFT (along with MID, RIGHT, and FIND) to create calculated fields is inefficient, requiring multiple complex formulas instead of a single, simple operation.

D. The Aliases option: The Aliases option is incorrect because it is used to rename specific member values within a field (e.g., changing "US" to "United States"), not to restructure data by splitting a column.

1. Tableau Prep Help - Split a Field into Multiple Fields: "If your data includes fields that contain multiple units of information

you can split the values in the field into new fields. For string values

you can split the data using the built-in split options or create a custom split. Tableau Prep Builder can automatically detect the common separator and split the field for you." This directly supports using the Split option for the given scenario.

Source: Tableau Official Documentation

"Split a Field into Multiple Fields".

2. Tableau Prep Help - Create Calculated Fields in Tableau Prep: This document outlines the process for creating calculated fields. While it lists functions like LEFT

RIGHT

and TRIM

the process of using them to parse a string is multi-step and complex compared to the dedicated Split feature

making it less efficient.

Source: Tableau Official Documentation

"Create Calculated Fields in Tableau Prep".

3. Tableau Prep Help - Edit Field Values: This document explains the use of Aliases. "You can directly edit a value in a field or use Find and Replace to replace multiple values... This is similar to creating an alias in Tableau Desktop." This confirms that Aliases are for renaming values

not splitting columns.

Source: Tableau Official Documentation

"Edit Field Values".