Salesforce Analytics-Con-301 Exam Dumps - Actual Questions Answers

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.

To calculate the Total Sales Value for all categories as displayed in the table, an LOD expression is ideal. An LOD calculation in Tableau allows you to compute values at the data level that is different from the view level. In this case, since the Total Sales Value appears consistent across different sub-categories within each category, an LOD expression can be used to fix the Total Sales Value irrespective of the sub-category detail. Here's how to set it up:

Go to the Calculations area by right-clicking in the data pane and selecting "Create Calculated Field".

Enter a name for the calculation, such as "Total Sales Value".

Enter the LOD expression: { FIXED [Category] : SUM([Sales]) }. This calculation fixes the total sales to the category level, effectively summing sales for all sub-categories within each category, irrespective of how the data is broken down in the view.

Drag this new calculated field into your visualization alongside the existing measures.

This method ensures that the Total Sales Value reflects the total for each category across all its sub-categories, matching the uniform values shown across different rows for each category in your table.

References

The explanation utilizes the concept of Level of Detail calculations in Tableau, which allows for advanced aggregations independent of the view level details. This concept is covered extensively in Tableau's official documentation and relevant training materials such as Tableau's online help resources.

Comprehensive and Detailed Explanation From Exact Extract:

The key requirements:

Tableau Cloud

Extracts (not live data)

Need RLS

Want low maintenance

Have Tableau Data Management

These requirements point directly to:

Data Policies (Virtual Connections RLS)

Tableau Data Management enables Virtual Connections and Data Policies, which provide:

Centralized row-level security

Integration with user identity via USERNAME()

Reusable RLS logic across all downstream workbooks

Works with extracts, unlike database RLS

Minimal long-term maintenance

This is Tableau’s recommended enterprise RLS method for Tableau Cloud with extracts.

Why the other options are incorrect:

A. Built-in RLS in Snowflake

Only works with live connections.

This client uses daily extracts, so database RLS is bypassed.

C. Manual user filter

High maintenance, must be edited manually per user — not scalable.

D. Dynamic user filter

Functional but must be recreated in each workbook, not centralized.

More maintenance than a data policy.

Therefore, Data Policy is the only low-maintenance, centralized RLS solution.

Data Policies and Virtual Connections documentation describing centralized RLS

Tableau Cloud & Extracts guidelines showing that database RLS cannot be reused

RLS strategy best practices recommending Data Policies for scalable governance