Salesforce Certified Tableau Consultant Analytics-Con-301 Exam Questions

Answer : A

To compute the required calculations and update the worksheet in Tableau Desktop, follow these steps:

Compute Average Sales by Segment for Each Category and Year:

Open the CC workbook and navigate to the Categorical Sales worksheet.

Drag the 'Sales' field to the Rows shelf if it's not already there.

Drag the 'Segment' field to the Rows shelf as well, placing it next to 'Category' and 'Year'.

Right-click on the 'Sales' field in the Rows shelf and select 'Quick Table Calculation' > 'Average'. This will compute the average sales for each segment within each category and year.

Create a Calculation for Year-over-Year Percentage Change:

Right-click in the data pane and select 'Create Calculated Field'.

Name the calculated field something descriptive, e.g., 'YoY Sales Change'.

Enter the formula to calculate the year-over-year percentage change:

(ZN(SUM([Sales])) - LOOKUP(ZN(SUM([Sales])), -1)) / ABS(LOOKUP(ZN(SUM([Sales])), -1))

Click 'OK' to save the calculated field.

Replace the Original Measure with the Year-over-Year Percentage Change in the Crosstab:

Remove the original 'Sales' measure from the view by dragging it off the Rows shelf.

Drag the newly created 'YoY Sales Change' calculated field to the Rows shelf where the 'Sales' field was originally.

Format the 'YoY Sales Change' field to display as a percentage. Right-click on the field in the Rows shelf, select 'Format', and adjust the number format to percentage.

Save Your Changes:

From the File menu, click 'Save' to ensure all your changes are stored.

Tableau Help: Offers guidance on creating calculated fields and using table calculations.

Tableau Desktop User Guide: Provides instructions on formatting and saving worksheets.

These steps allow you to manipulate data within Tableau effectively, using table calculations to analyze trends and changes in sales data by category and segment over years.

Topic 3, Knowledge-Based Section B

Answer : D

The issue occurs because of Tableau's Order of Operations.

Key Tableau logic:

Top N filters are a type of Dimension filter.

Dimension filters are evaluated after Context filters.

When you place Region on Filters (as a standard dimension filter), Tableau:

First applies the Customer Name Top 10 filter across the entire data set, not per region.

Then limits the view to the selected region(s).

This results in seeing the global Top 10 customers, not the Top 10 per region.

How to fix it:

To force Tableau to compute Top 10 customers within each region, the Region filter must be applied before the Top N Customer filter.

This is done by making Region a Context Filter.

Effect of a Context Filter:

Context filters are executed before the Top N filter.

Region becomes the context.

Tableau then evaluates the Top 10 customers inside each region's subset of data.

This produces the correct ''Top 10 customers per region''.

Why the other options are incorrect:

A . Extract filter

Applies once when creating the extract; does not control Top N logic inside the workbook.

B . Dimension filter

This is what the analyst already has --- and it causes the unwanted behavior because it happens after the Top N filter.

C . Table Calculation filter

Top N is not a table calculation; table calc filters cannot fix this problem.

Only the Context Filter changes the execution order so Top N works per region.

Tableau Order of Operations showing Context Filters applied before Top N filters.

Best practices recommending Context Filters when Top N must be computed within subcategories.

Filtering documentation explaining that Top N filters require context when additional dimensional filters are present.

Answer : A, C

Comprehensive and Detailed Explanation From Exact Extract:

The performance degradation originates from string parsing inside Tableau ('last word of Full Name') and then feeding that calculated field into another row-level IF-THEN calculation.

This creates:

Nested calculations

High per-row evaluation load

Slow extract query performance or slow live query generation

Tableau documentation recommends two best-practice approaches:

Solution 1: Precompute the ''Last Name'' field upstream (Option C)

When the parsing is performed in:

The database

ETL/ELT pipelines

Tableau Prep

then Tableau Desktop receives a clean field with no runtime computation needed.

This significantly reduces row-level calculation burden.

Solution 2: Replace Quick Filters with Action Filters (Option A)

Quick filters are expensive because Tableau:

Runs additional queries to populate filter controls

Re-queries every time the filter changes

Action Filters run directly from the visualization and are far more performant.

This improves the overall dashboard performance without changing logic.

Why the other options are incorrect:

B . Calculate ''Last Name'' inside the IF THEN calculation

This makes the expression even more complex --- worse performance.

D . Change to a CASE statement

CASE does not improve performance when the heavy part of the logic is the string parsing, not the IF-THEN structure.

Thus, A and C are the correct performance-improving choices.

Performance guidance recommending upstream computation of string fields

Filter optimization best practices encouraging Action Filters over Quick Filters

Extract runtime cost reduction strategies

Answer : B

For optimal performance, it is recommended to limit the data to what is necessary for analysis, which in this case would be the current and previous years. Moving the calculation of averages to the data layer and aggregating the dates to a monthly level will reduce the granularity of the data, thereby improving the performance of the dashboard. This approach aligns with best practices for optimizing workbook performance in Tableau, which suggest simplifying the data model and reducing the number of records processed12.

Answer : D

Comprehensive and Detailed Explanation From Exact Extract:

The key detail of the question:

''filter out particular categories, but wants the percent of total calculation to remain steady even as they filter items in or out.''

This means the percent of total must ignore filters.

Table calculations always operate after filters, except table calc filters like 'Filter on Table Calculation,' and after dimension filters, so filtering categories directly will change the denominator.

Tableau's documented solution for ''percent of total that does not change with filtering'' is:

Use a FIXED LOD to define the stable denominator

A FIXED LOD expression ''freezes'' the aggregation level and is unaffected by dimension filters unless explicitly added to context.

This allows the consultant to compute:

{ FIXED : SUM([Sales]) }

or

{ FIXED [Category] : SUM([Sales]) }

Then percent of total becomes:

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

The FIXED LOD stores the total before filters are applied, ensuring the percent remains steady.

This is exactly what Tableau documentation explains under:

Level of Detail Expressions

LODs and Order of Operations

Using LODs to create filter-independent calculations

Thus, D is correct.

Why the other answers are wrong:

A. Context Filter

Context filters run before FIXED LODs but after raw data.

If Category is put into context, LOD totals would be reduced.

Table calculation totals still change because table calcs run near the bottom of the pipeline.

B. Data Source Filter

Data source filters remove rows before all table calculations and LODs.

This would make the percent of total incorrect, because filtered-out categories would physically be gone.

C. Aggregate Expression

An aggregate field alone does not solve the issue because it still respects dimension filters.

Answer : B, C

Comprehensive and Detailed Explanation From Exact Extract:

Two Tableau Server features provide usage and adoption insights:

Tableau Server Repository

Stores all metadata about:

Workbooks

Data sources

User activity

View traffic

Can be queried directly for content usage and platform metrics.

Admin Insights Page

Built-in dashboards showing:

User activity

Content usage

Data source usage

Performance metrics

Designed specifically for monitoring platform adoption.

These two together give complete content and usage visibility.

Why A and D are incorrect:

A . Tableau Pulse

Available only in Tableau Cloud, not Tableau Server.

Focuses on personalized metric insights, not platform reporting.

D . Server Status Page

Shows node health and process status, not content usage or adoption analytics.

Thus, correct answers are B and C.

Tableau Server auditing and usage documentation describing repository tables.

Admin Insights documentation describing built-in content and user monitoring.

Answer : A, C

Comprehensive and Detailed Explanation From Exact Extract:

The key requirement is:

Data must remain at the original grain in Tableau Prep

Tables are at different granularities

Tableau Prep does NOT support relationships, and automatically joins tables, which changes granularity (by duplicating or aggregating records).

Therefore, relationships (Option B) cannot preserve grain for Prep.

Also:

A physical join (Option D) changes the grain by combining rows, often multiplying results when grain differs.

Only two options preserve the original granularity:

Option A --- Two Separate Published Data Sources

Each data source represents one table.

In Tableau Prep, the user can choose:

Use tables separately

Join or clean them intentionally

Keep each table at its own grain

This keeps all data at its native level.

Option C --- Virtual Connection

A Virtual Connection:

Publishes entire tables from the database

Maintains each table independently at its native granularity

Makes all tables available to Tableau Prep without altering grain

Is specifically designed for governed, reusable multi-table access

Thus, it satisfies the requirement exactly.

Why the others are incorrect:

B --- Relationship

Relationships only exist in Tableau Desktop logical layer, NOT in Tableau Prep.

Prep flattens the data grain is lost.

D --- Physical join

Always modifies granularity when tables differ, often causing row multiplication.

Tableau Prep does not support logical relationships; only physical joins.

Virtual Connections preserve original tables and governance.

Published Data Sources can be separated to maintain original grain.