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When dashboards are published to Tableau Cloud, the function USERNAME() returns the user’s Tableau Cloud username, which is the email address associated with their Tableau Cloud account.

Tableau documentation states:

On Tableau Cloud, the value returned by USERNAME() is always the user’s email address.

Row-Level Security (RLS) is typically implemented using a comparison of USERNAME() to an email field in the data source.

For secure filtering, the field compared to USERNAME() must match the authentication identity exactly.

Looking at the provided table:

“Abbreviated Name” contains short custom codes like “SMiller,” which do not match Tableau Cloud usernames.

“Lower Case Name” contains names like “sean miller,” which also do not match Tableau Cloud usernames.

“Email” contains the full email address for each user, such as “Sean.Miller@superstore.com,” which is the only field that corresponds to what USERNAME() returns in Tableau Cloud.

Therefore, the correct field to reference is Email.

Tableau Cloud authentication documentation stating USERNAME() returns the user’s email address.

Row-Level Security setup guidance recommending the comparison USERNAME() = [Email Field].

Tableau security practices indicating only the email column will match USERNAME() values on Tableau Cloud.

Comprehensive and Detailed Explanation From Exact Extract:

A LOOKUP table calculation requires access to rows outside the filtered date range so that the calculation can reference prior data. When a Relative Date Filter removes older data before the table calculation is evaluated, the LOOKUP loses the needed rows, causing the prior year’s data to disappear.

Tableau’s order of operations states:

Relative Date Filters act early (at the dimension filter stage).

Table calculations act very late.

To preserve table calculation context, filters must not remove necessary rows.

Two Tableau-documented approaches address this:

Explanation for B

Setting the Relative Date Filter as a Context Filter allows table calculations to operate on the full dataset needed for LOOKUP. Context filters create a separate temporary table, and subsequent filters like table calculations evaluate after the context is established.

This ensures older rows are still available to the LOOKUP function.

Explanation for C

Creating a field such as:

LOOKUP(MIN([Order Date]), 0)

and filtering on this field instead of Order Date converts the filter into a table calculation filter, which occurs after the LOOKUP computation. Tableau documentation explains that table calculation filters preserve the full dataset required for the LOOKUP window.

This ensures that the LOOKUP still has access to last year's values even when filtering for the current 12 months.

Why A is incorrect

Replacing LOOKUP with an LOD changes the logic entirely.

LOD expressions cannot replicate moving-window or lag-type behavior.

Why D is incorrect

DATEDIFF logic can replicate a rolling window, but hiding False values is essentially a manual filter and does not preserve the integrity of the LOOKUP’s required partitioning. It also contradicts Tableau’s recommended approach for maintaining table calculation context.

Tableau Order of Operations explaining why table calculation filters preserve data for LOOKUP.

Tableau documentation on context filters and how they allow more data to remain available for downstream table calculations.

Tableau guidance on how Relative Date Filters interact with table calculations.

Best practices for preserving table calculation window rows when filtering.

To effectively display quarterly profit growth and aggregated sales totals across different product categories, a combination of a Waterfall chart and a Tree Map is recommended:

Waterfall Chart: This chart type is excellent for visualizing the sequential growth or decline of profits across different quarters for each sub-category. It clearly shows how profits accumulate over time, highlighting both positive and negative changes, which makes it ideal for tracking profit growth or decline through the quarters.

Tree Map: A Tree Map can efficiently display aggregated sales totals where each block size represents the total sales of a product category, providing a quick, visually impactful comparison across categories. This is especially useful when the client wants to understand which categories contribute most to sales in a glanceable format.

Together, these charts provide a comprehensive overview of both profit trends over time (Waterfall Chart) and a comparative snapshot of sales performance across categories (Tree Map), meeting the client's need to analyze performance dynamics in a detailed yet consolidated manner.

References

These recommendations are based on common best practices for data visualization in Tableau, where specific chart types are chosen for their strengths in communicating certain types of data relationships and dynamics, as detailed in Tableau's official visualization guides.

The problem:

The client wants:

Percent of total sales for each region compared to ALL regions,

even when Region is filtered.

However, the calculation currently behaves like a table calculation:

SUM([Sales]) / TOTAL(SUM([Sales]))

This recalculates the total after Region filters are applied, so removing a region changes the denominator.

Tableau Documentation — How to prevent recalculation:

To keep percent-of-total unchanged when filtering, Tableau’s recommended method is to use FIXED LOD expressions to lock the granularity.

Two values must be fixed:

Numerator: Sales for that specific region{ FIXED [Region] : SUM([Sales]) }

Denominator: Total sales across all regions, independent of filters{ FIXED : SUM([Sales]) }(FIXED with no dimension = entire data set)

Then compute the percentage:

{ FIXED [Region] : SUM([Sales]) } / { FIXED : SUM([Sales]) }

This ensures:

The region sales remain accurate.

The overall total remains constant, even if filters remove regions.

Region filtering no longer recalculates percent-of-total.

Why the other options are incorrect:

A. {FIXED [Region]: SUM([Sales])} / SUM([Sales])

The denominator is still affected by filters → recalculates % of total.

B. {FIXED [Region]: SUM([Sales])} / { [Sales] }

{[Sales]} is not valid syntax and does not fix granularity.

D. {FIXED [Region]: SUM([Sales])}

This gives only the numerator — no percent-of-total calculation.

The only correct LOD solution is option C.

Tableau LOD Expression Guide: FIXED for filter-independent calculations.

Tableau Percent-of-Total Best Practices: use FIXED LOD to avoid recalculation when filters change.

Order of Operations: FIXED LODs occur before dimension filters, keeping totals stable.

Comprehensive and Detailed Explanation From Exact Extract:

The requirement is:

Compute total sales per customer, not per transaction.

Then compute the average of those customer totals, grouped by region.

Tableau documentation states that FIXED LOD expressions are used to calculate values at a specific level of granularity regardless of the view.

To solve the business need:

Step 1:

Calculate total customer sales for each Customer ID within each Region:

{ FIXED [Customer ID], [Region] : SUM([Sales Amount]) }

This produces one number per customer per region.

Step 2:

Compute the average of those totals:

AVG( { FIXED [Customer ID], [Region] : SUM([Sales Amount]) } )

This yields:

Average total customer sales by region

This is exactly option C.

Why the other options are incorrect:

A. EXCLUDE Region: Would combine regions and incorrectly calculate overall totals.

B. FIXED Region: AVG(Sales Amount): Computes average of line-level sales, not customer totals.

D. FIXED Customer ID + Region: AVG(Sales Amount): Averages individual transactions, not customer totals.

Only option C matches the required two-step logic.

LOD Expressions: FIXED for computing customer-level aggregates.

Nested LOD usage for first calculating customer totals, then averaging them at a higher level.

Tableau guidance: SUM inside FIXED for per-customer totals, AVG outside for averaging customers.

Comprehensive and Detailed Explanation From Exact Extract:

The dataset contains timestamps for each individual user interaction. Growth in user adoption over 6 months means the number of rows has expanded significantly. Tableau’s performance documentation states that large row-level datasets can cause performance degradation even when using extracts, especially when:

The visualization is aggregated to a higher level (such as daily), and

The underlying extract still contains much more granular data than needed.

Tableau recommends pre-aggregating data before it reaches Tableau Desktop, which reduces extract size, memory use, and query time. This improves performance without changing what the visualization displays.

Option D uses Tableau Prep, which is included with Tableau Creator licensing and therefore incurs no additional cost. Tableau Prep can aggregate raw timestamp data into daily totals per product feature, which matches the visualization’s actual granularity. This results in:

A dramatically smaller extract

Faster queries

No change to how the dashboard looks or functions

Option A would remove product features from the visualization, altering the dashboard content and reducing insight, which does not meet the requirement of minimal impact.

Option B requires purchasing an external ETL tool, which violates the requirement of no additional cost.

Option C reduces the number of extract refreshes but does not improve dashboard performance; the data would remain equally granular and equally slow.

Therefore, Tableau Prep aggregation is the correct solution that improves performance while maintaining the same visualization and incurring no additional cost.

Tableau performance guidelines recommending pre-aggregation of highly granular datasets.

Tableau Prep documentation stating it can be used to aggregate data before creation of extracts.

Tableau’s extract optimization guidance describing how reducing row counts improves query and visualization performance.

When “Apply to Nested Projects” is checked in Tableau Server, the permission rules set at the top-level project are enforced for all assets in the project and all nested projects. This means that any custom access previously granted to nested projects will be overridden, and the permissions will revert to those defined at the top-level project. This action ensures consistent application of the data governance strategy across all divisions.

Given the need for up-to-date data on a daily basis and weekly revisions, the best approach is to use an incremental extract refresh daily to update the data source with new transactions. On Saturdays, when incorrect transactions are revised, a full extract refresh should be performed to incorporate all revisions and ensure the data’s accuracy. This strategy allows end users to have access to the most current data throughout the week while also accounting for any necessary corrections12.

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.

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.