The key requirement is to create a calculation that is independent of locale settings. The DATEPART function with the iso-weekday part meets this requirement. It adheres to the ISO 8601 standard, where the week consistently starts on Monday (1) and ends on Sunday (7).

In this standard:

Saturday is always represented by the integer 6.

Sunday is always represented by the integer 7.

Therefore, the expression DATEPART('iso-weekday', [Order Date]) >= 6 correctly and efficiently identifies both Saturday and Sunday, regardless of any regional or data source settings for the start of the week.

A. The DATEPART('weekday', ...) function's result depends on the data source's configured start of the week, making it unreliable across different locales.

C. The DATENAME function returns a string (e.g., "Saturday"), which causes a type mismatch when compared numerically with >= 6.

D. This expression checks if the day is Monday (iso-weekday = 1) or Sunday (iso-weekday = 7), which does not match the requirement to identify Saturday and Sunday.

1. Tableau Official Documentation

"Date Functions": This document details the behavior of date functions used in Tableau and related Salesforce Analytics products.

Section on DATEPART(datepart

date

[startofweek]): It specifies that 'iso-weekday' returns the weekday as an integer from 1 to 7

where Monday is 1. This confirms that Saturday is 6 and Sunday is 7. It also clarifies that the standard 'weekday' part is dependent on the [startofweek] parameter

making it locale-sensitive. (Accessed via Tableau Help portal).

2. Salesforce Help

"Tableau CRM Functions in Data Prep Recipes": While syntax can vary

the principle of using standardized functions for consistency is a core concept. The ISO 8601 standard is the universal solution for locale-independent week calculations. The use of iso-weekday in the question points directly to the Tableau function set

which is part of the ANALYTICS-CON-301 curriculum.

This solution addresses both of the client's goals: performance and accuracy. By creating an "Unknown" record in the Browser Table and replacing NULL values in the Site Visitor Table with the new "Unknown" BrowserID, every record in the fact table (Site Visitor) now has a corresponding entry in the dimension table (Browser). This allows the Referential Integrity setting to be changed to "All Records Match." This setting is more performant because CRM Analytics can make optimizations during query execution, knowing that a match for every record is guaranteed. This approach also ensures an accurate count of all site visitors, as those with unknown browsers are now explicitly categorized instead of being represented by a NULL key.

A. This is the current configuration. It does not implement any changes to improve dashboard performance as requested by the client.

C. While this option correctly cleanses the data by adding an "Unknown" record, it fails to capitalize on the performance gain by not changing the Referential Integrity setting.

D. Changing the setting to "All Records Match" while NULL values exist would cause data loss. The join would act as an inner join, dropping all site visitor records with a NULL BrowserID, leading to an inaccurate visitor count.

---

1. Salesforce Help: Join Data in a Recipe. This document explains the "Referential Integrity" setting within CRM Analytics Recipes. It states

"For better performance

select All Records Match if you’re certain that every key in the left source has a matching key in the right source." This supports the performance improvement aspect of the correct answer. The document also clarifies that "Some Records Match" should be used when a key might not have a match

which is the case with NULL values

confirming why option D is incorrect.

Source: Salesforce Official Documentation

"Join Data in a Recipe

" section on Join Keys and Referential Integrity.

2. Salesforce Help: Data Integration Guide - Best Practices. While not a single page

data integration best practices consistently recommend cleansing data to avoid null foreign keys. Replacing them with a default or "sentinel" value (like an "Unknown" record ID) is a standard data warehousing technique to ensure data integrity and enable more efficient join operations. This principle underpins the logic of creating an "Unknown" record to allow for the "All Records Match" optimization.

Source: Salesforce CRM Analytics Documentation

principles of data preparation and optimization.

The most efficient and direct method to control who can see a published report (workbook) on Tableau Server is by managing content permissions. The scenario states that user groups for "sales" and "marketing" already exist. By granting the 'View' permission for the report specifically to these two groups and denying it to others, the client achieves the goal. This server-side permissioning is the standard mechanism for controlling access to content itself, as opposed to filtering the data within the content. It is highly efficient because it leverages the existing group structure and requires a simple configuration on the server without altering the workbook.

B: Row-level security (RLS) controls which rows of data a user sees inside a report, not whether they can access the report itself. This is overly complex for the stated goal.

C: Applying user filters to each worksheet is inefficient and controls data visibility, not report access. Users outside the groups could still see the report, albeit with no data.

D: A user filter on the data source is for data-level security. It does not prevent users from other groups from discovering and opening the report on the server.

1. Tableau Help

"Permissions": This document outlines the fundamental concepts of content permissions in Tableau. It states

"Permissions are rules that you set on Tableau Server or Tableau Cloud to control which users or groups can access content... Permissions consist of capabilities—the ability to view

connect

web edit

and so on—allowed or denied for a user or group." This directly supports option A as the correct method for controlling access to the report.

Source: Salesforce

Tableau Help

"Permissions"

Section: "How permissions are evaluated".

2. Tableau Help

"Set Permissions on Individual Content Resources": This guide provides the specific steps for the action described in option A. It details how an administrator or content owner can select a workbook

edit its permissions

and assign capabilities (like 'View') to specific users or groups.

Source: Salesforce

Tableau Help

"Set Permissions on Individual Content Resources"

Section: "Edit permissions for a piece of content".

3. Tableau Help

"Restrict Access at the Data Row Level": This document explains the purpose of row-level security (RLS) and user filters

which are the mechanisms described in options B

C

and D. It clarifies that RLS is used "so that users can view only the data that they have permission to view." This confirms that RLS is for filtering data within a view

contrasting with content permissions that control access to the view.

Source: Salesforce

Tableau Help

"Restrict Access at the Data Row Level"

Introduction.

The Performance Recording indicates that "Executing Query" is the longest-running event, meaning the query sent to the data source is the primary performance bottleneck. A Relative Date filter on a continuous date field (Created Date) over a five-year span forces the database to evaluate a wide range of values. Replacing this with a filter on a discrete, calculated field like YEAR([Created Date]) significantly improves performance. This change simplifies the query's WHERE clause, allowing the database to filter on a small set of integer values (the years) instead of a large, continuous range of dates, which is a much more efficient operation.

A. Replacing the filter type addresses the performance of rendering the filter control itself, not the "Executing Query" event, which is the identified bottleneck.

C. TOTAL() is a table calculation computed after the main query returns data. Modifying it will not improve the initial query execution time.

D. Replacing a simple aggregation (SUM) with a table calculation (WINDOWSUM) would shift computation from the database to Tableau and likely worsen performance, not improve the query time.

1. Tableau Desktop and Web Authoring Help

"Optimize Workbook Performance":

Section: "Filter efficiently" -> "Filter by creating a discrete field"

Content: "When you filter on a continuous date

you can select a range of dates

which requires a database query. But if you filter on a discrete date

you can select specific dates

which is a more efficient query." This directly supports the principle behind replacing a continuous date filter with a discrete one (like YEAR) to improve query performance.

2. Tableau Whitepaper

"Designing Efficient Workbooks":

Section: "Filters" (typically found under "Data Source and Query Performance")

Content: The document emphasizes that filters on discrete fields with lower cardinality (like years) tend to perform better than filters on continuous fields with high cardinality (like a full date range). This is because the resulting query is simpler and can be processed more quickly by the underlying data engine (Hyper

in this case).

3. Tableau Desktop and Web Authoring Help

"Table Calculations: Addressing and Partitioning":

Section: "Performance"

Content: This documentation explains that table calculations like TOTAL() and WINDOWSUM() operate on the table of aggregated data that has been returned to Tableau. Since they are post-query calculations

optimizing them does not resolve a bottleneck in the "Executing Query" phase. This confirms why options C and D are incorrect.

A Sankey diagram is the most appropriate visualization for this requirement. It is specifically designed to illustrate the flow and magnitude of transitions between different states or stages. In this scenario, it can effectively map the journey of students, showing how the distribution of majors changes from one grade level (e.g., Freshman) to the next (e.g., Sophomore). The width of the connecting paths (links) between the nodes (majors) would be proportional to the number of students, clearly visualizing the paths, including where students remain in the same major and where they switch.

A. Chord Chart: A chord chart is used to visualize relationships and flows between a single set of entities, not a sequential, multi-stage progression across different grade levels.

B. Tree Chart: A treemap is designed to display hierarchical data and part-to-whole relationships using nested rectangles. It cannot represent the flow or transition of data between categories.

C. Radar Chart: A radar chart is used for comparing multiple quantitative variables for one or more series. It is unsuitable for visualizing flow or paths between categorical states.

1. Salesforce Help Documentation

CRM Analytics Chart Types:

Sankey Diagram: "Visualizes the distribution of a measure from one grouping to another grouping. The groupings are the source and destination dimensions." This directly aligns with visualizing the distribution of students (measure) from majors in one grade level (source) to majors in the next (destination).

Reference: Salesforce Help. (2023). CRM Analytics Chart Types. Retrieved from https://help.salesforce.com/s/articleView?id=sf.bicharttypes.htm&type=5

2. Salesforce Help Documentation

Create a Sankey Diagram:

Use Case: "Use a sankey diagram to visualize the flow of data from one category to another. For example

identify how leads from different campaigns flow through the lead status." This example of tracking flow through stages (lead status) is analogous to tracking students through stages (grade levels and majors).

Reference: Salesforce Help. (2023). Create a Sankey Diagram. Retrieved from https://help.salesforce.com/s/articleView?id=sf.bicharttypesankeycreate.htm&type=5

This strategy correctly implements a "what-if" analysis, which is the core of the client's request. A parameter allows the end-user to select a shipping vendor dynamically. A calculated field then references this parameter to perform a row-level calculation, determining the cost for each individual order based on the selected vendor's specific rates. Finally, Tableau aggregates these individual costs to display the total. This approach is highly performant because the complex calculation is only executed for the single, user-selected scenario, directly addressing the critical speed requirement.

A. This option avoids the technical challenge by changing the business requirements. It fails to provide a dashboard building strategy as requested.

B. Pre-calculating costs for every vendor for every order can create a very large and wide dataset, leading to slow query and load times, which violates the critical speed requirement.

C. Aggregating orders before applying the cost calculation is logically flawed. Shipping costs are typically determined per order based on its specific attributes (volume, speed), and this necessary detail would be lost through pre-aggregation.

1. Tableau Help

"Create Parameters"

Section: "Parameters in calculations". Official documentation states

"A common use for parameters is in calculations. You can reference the parameter in the calculation

and then you can show the parameter control in the view for users to select." This supports using a parameter for vendor selection to drive a calculation.

2. Tableau Help

"Swap Measures Using Parameters". This document provides a direct parallel to the required solution. While the example swaps measures like Sales and Profit

the principle is identical: using a parameter and a CASE statement within a calculated field to dynamically change the logic based on user input. The question requires swapping the cost calculation logic based on the selected vendor.

3. Tableau Help

"Use Parameters to Add More Interactivity to Your Views". This guide explains how parameters allow users to "input values into calculations to see how the data changes." This is the exact functionality needed for the client to model shipping costs by selecting different vendors. The document emphasizes this as a method for creating interactive

what-if scenarios.

The most effective and recommended approach is to build a single dashboard and utilize the built-in device layout feature. This allows a consultant to create customized layouts for desktop, tablet, and phone experiences within one dashboard asset. By tailoring the arrangement and visibility of components for each specific layout and using a fixed size for predictability, the consultant can ensure an optimal and consistent user experience across all devices. This method centralizes maintenance, as any change to the underlying data or queries only needs to be done once.

B. Build one dashboard and fix the size of the dashboard.

A single fixed-size dashboard will not adapt to different screen sizes, resulting in scrollbars or poorly utilized space, which degrades the user experience on most devices.

C. Build one dashboard and set the size to Automatic.

Automatic sizing can cause unpredictable resizing and distortion of dashboard components, especially when viewed on screens with vastly different aspect ratios, leading to a poor user experience.

D. Build one dashboard for each type of device and fix thesize of the layouts.

This approach is highly inefficient. Managing three separate dashboards creates a significant maintenance overhead, as any update must be replicated across all three versions.

1. Salesforce Help Documentation

"Optimize Dashboards for Different Devices": This document explicitly states

"Create layouts for the different devices that your users have. You can create a single dashboard and then add layouts that are optimized for viewing on desktops

tablets

and phones... Each layout can have its own widgets

with their own customizations." This directly supports creating one dashboard with multiple

specific layouts.

2. Tableau Help Documentation

"Create Dashboard Layouts for Different Device Types": (The underlying technology for CRM Analytics dashboards). This guide details the process: "You can design

customize

and publish a single dashboard that includes unique layouts for desktop computers

tablets

and phones... When you publish a dashboard with device layouts

people who view it... see a layout optimized for their screen." This confirms that a single dashboard with multiple layouts is the intended and optimal method.

3. Tableau Help Documentation

"Size and Lay Out Your Dashboard": This document contrasts different sizing options. It explains that a "Fixed" size provides control

which is essential for device-specific layouts

while "Automatic" sizing "can cause views to be resized or scrunched in unexpected ways

" making it unsuitable for delivering the best user experience.

The goal is to compute an aggregate (SUM(Sales)) at a different, less granular level of detail (Category) than what is displayed in the visualization (Category and Sub-Category). A Level of Detail (LOD) expression is the precise tool for this requirement. The specific calculation, {FIXED [Category] : SUM([Sales])}, instructs Tableau to compute the total sales for each category and return that value for every row belonging to that category, independent of other dimensions like Sub-Category in the view.

A. Quick Table Calculation: Standard quick table calculations, such as running totals or moving averages, operate on the visible data in the view and do not compute aggregates at a fixed, independent level of detail.

B. A Window Function: While a window function like WINDOWSUM(SUM([Sales])) could replicate this result, it is a table calculation. Its output is dependent on the visualization's structure and is less portable than an LOD expression.

D. MAX() Function: The MAX() function is a standard aggregation that would find the single largest sale value. It does not sum values to calculate a total for a category.

1. Tableau Official Documentation

"Create Level of Detail Expressions in Tableau": This document explains the core purpose of LOD expressions. It states

"Level of detail expressions allow you to compute values at the data source level and the visualization level... you can compute aggregations that are not at the level of detail of the visualization." The FIXED keyword is shown to compute a value using only the specified dimensions.

Source: Tableau Help

https://help.tableau.com/current/pro/desktop/en-us/calculationscalculatedfieldslod.htm

2. Tableau Official Documentation

"FIXED Level of Detail Expressions": This page provides a specific example similar to the question. It shows calculating a sum for a region while the view contains states. The syntax {FIXED [Region] : SUM([Sales])} is used to compute the sum of sales at the region level

which is then displayed for each state within that region.

Source: Tableau Help

https://help.tableau.com/current/pro/desktop/en-us/calculationscalculatedfieldslodfixed.htm

Section: "Example 1".

3. Tableau Official Documentation

"Table Calculation Functions": This document details how table calculations

including window functions

operate. It clarifies that these functions compute based on the "virtual table" of data in a visualization

making them dependent on the dimensions present in the view

unlike FIXED LODs.

Source: Tableau Help

https://help.tableau.com/current/pro/desktop/en-us/functionsfunctionstablecalculation.htm

Introduction paragraph.

This simulation first requires you to generate performance data by starting the recording (Help > Settings and Performance > Start Performance Recording), performing the specified actions, and stopping the recording.

Tableau then opens a new workbook containing this data. The core task is to build the specified bar chart. Placing the Worksheet and Command dimensions on the Rows shelf and the Elapsed Time measure on the Columns shelf correctly structures the data. This creates a bar chart that visually represents the duration of each command, broken down by the worksheet it originated from, thereby isolating the components contributing to the load time as requested.

Tableau Desktop and Web Authoring Help. (c. 2024). Record and Analyze Workbook Performance.

Section: "Create a performance recording in Tableau Desktop" confirms the procedure for starting and stopping the recording (Help > Settings and Performance).

Section: "Interpret a performance recording workbook" describes the resulting workbook. The Timeline view is noted as containing Workbook, Dashboard, and Worksheet columns, confirming the existence of the Worksheet field for analysis.

Tableau Desktop and Web Authoring Help. (c. 2024). Build a Bar Chart.

Section: "Build a Bar Chart" confirms the standard procedure: "You create a bar chart by placing a dimension on the Rows shelf and a measure on the Columns shelf, or vice versa." This validates the placement of Worksheet/Command (dimensions) on Rows and Elapsed Time (measure) on Columns.

Tableau Server Help. (c. 2024). Interpret a Performance Recording. (Note: The performance workbook structure is consistent with Desktop).

Section: "CPU and Elapsed Time" describes the "Elapsed Time views" which "provide aggregate statistics for each activity... the total amount of time taken by a single activity is shown using the bar chart." This confirms Elapsed Time is the correct measure for this analysis.

To display a cumulative percentage line, a multi-step table calculation is required. The primary Running Total calculation computes the cumulative sum of sales across the products. The secondary Percent of Total calculation then converts these cumulative sums into a percentage of the overall total. This result is placed on a second axis (Dual Axis), changed to a line mark, and synchronized with the primary axis to overlay the cumulative percentage line onto the original chart. This technique is a standard method for creating a Pareto chart to visualize cumulative contribution.

1. Salesforce

Tableau Desktop and Web Authoring Help. (2024). "Create a Pareto Chart". This official documentation provides the exact step-by-step instructions to add a running total percent of total line to a chart using a secondary table calculation and a dual axis

as required by the simulation.

2. Salesforce

Tableau Desktop and Web Authoring Help. (2024). "Transform Values with Table Calculations". Section: "Types of table calculations". This document defines the core functions used in the solution

specifically explaining the Running Total and Percent of Total table calculations.

3. Salesforce

Tableau Desktop and Web Authoring Help. (2024). "Build a Combination Chart with a Dual Axis". This document explains the concept and procedure for using dual axes to combine multiple measures with different mark types (e.g.

bars and lines) in a single view

which is a key part of the required solution.