Analytics-Con-301 Exam Dumps - Salesforce Certified Tableau Consultant

The dataset shown is a classic “wide” format”:

A single row per state

Separate columns for each year: 2019, 2020, 2021, 2022, 2023, 2024

Tableau’s documentation on data structure and pivoting explains:

✔ Why A is TRUE

Tableau documentation identifies wide datasets (multiple columns representing categories such as years, months, or similar time periods) as high-maintenance structures because:

For every new year, a new column must be added.

Metadata and calculations must be updated each time.

This type of structure is described as having poor scalability and higher maintenance.

This dataset fits that exact description, so A is correct.

✔ Why C is TRUE

According to Tableau’s “Pivot Data from Columns to Rows” section:

Wide datasets can and should often be pivoted so that repeated columns (such as year columns) become rows.

Pivoting enables dynamic capabilities such as:

Year filters (year selector)

Time-series analysis

Consistent aggregations

Simplified calculations

Pivoting this dataset would produce:

State

Year

Value

Alabama

2019

2300.39

Alabama

2020

3030.39

…

…

…

This makes the dataset tall and tidy, which Tableau identifies as better for analysis and dashboard interactivity.

Therefore, C is correct.

✘ Why B is FALSE

The column names (2019, 2020, 2021…) are simply numbers.

Tableau documentation stresses that good metadata includes descriptive column names.

These column names:

Do not indicate what the measure represents (Revenue? Sales? Population?)

Only show the year, not the meaning of the metric

Thus they are not considered accurate or descriptive column names.

✘ Why D is FALSE

The dataset is already denormalized, not normalized.

Denormalized data means combining multiple attributes (like multiple years) into one table, which is exactly what this dataset already does.

Tableau documentation explains that wide data is already denormalized, and the recommended fix is pivoting, not further denormalization.

Therefore, D is incorrect.

Comprehensive and Detailed Explanation From Exact Extract:

Tableau’s performance best-practice documentation explains that large workbooks containing many dashboards, multiple data sources, and complex interactions have heavier memory and CPU requirements. When a workbook grows too large, Tableau must:

Load every data source

Cache metadata for all sheets

Maintain connections across all dashboards

Render more worksheets simultaneously

This increases workbook load time and slows dashboard performance.

A documented method for improving performance is to split a large workbook into multiple smaller workbooks, each containing only the dashboards relevant to a particular audience or subject area. Smaller workbooks:

Reduce the amount of metadata Tableau must load

Reduce extract sizes per workbook

Improve caching efficiency

Improve dashboard loading and rendering time

Options A, B, and C do not improve workbook performance:

A. Converting Data Source Filters to Quick Filters reduces performance because Quick Filters (interactive filters) are more expensive than Data Source filters and slow down rendering.

B. Converting extracts to live connections typically worsens performance because live sources depend on database query speed, network latency, and server load.

C. Restricting users does not change workbook performance, only access.

Splitting the workbook is a recognized Tableau strategy for improving performance of large, multi-dashboard workbooks.

Tableau Performance Optimization guidelines encouraging splitting large workbooks into smaller ones.

Workbook design best practices emphasizing reduced complexity and smaller metadata footprints.

Documentation on performance costs associated with Quick Filters and multiple data sources.

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

Comprehensive and Detailed Explanation From Exact Extract:

Tableau documentation clearly defines user capabilities per license:

Viewer

Can view dashboards only.

Cannot create Data-Driven Alerts.

Cannot perform ad hoc analysis.→ Insufficient.

Explorer

Can view dashboards.

Can create Data-Driven Alerts.

Can perform ad hoc analysis using existing published data sources.

Does NOT require full creator-level capabilities.→ Meets all requirements at lower cost than Creator.

Creator

Full authoring, data source creation, and prep capabilities.

Higher cost.→ Overkill and violates “minimizes licensing costs.”

Site Administrator

Includes Creator capabilities plus admin privileges.

Most expensive.→ Not appropriate for managers.

Therefore, the correct and most cost-efficient role is Explorer.

Tableau role capability matrix showing Explorers can create alerts and analyze existing data sources.

Licensing guidance recommending Explorer for ad hoc analysis without data-source creation.

To correctly calculate the percent of total sales for a particular region compared to all regions, and to ensure that the calculation does not get inadvertently recalculated with each region filter application, the recommended calculation is:

{FIXED [Region]: sum([Sales])}: This part of the formula computes the sum of sales for each region, regardless of any filters applied to the view. It uses a Level of Detail expression to fix the sum of sales to each region, ensuring that filtering by regions won't affect the calculated value.

SUM([Sales]): This part computes the total sum of sales across all regions and is recalculated dynamically based on the filters applied to other parts of the dashboard or worksheet.

Combining the two parts: By dividing the fixed regional sales by the total sales, we get the proportion of sales for each region as compared to the total. This calculation ensures that while the denominator adjusts according to filters, the numerator remains fixed for each region, accurately reflecting the sales percentage without being affected by the region filter directly.

References

This calculation follows Tableau’s best practices for using Level of Detail expressions to manage computation granularity in the presence of dashboard filters, as outlined in the Tableau User Guide and official Tableau training materials.