Salesforce Salesforce Certified Tableau Consultant Analytics-Con-301 Exam Dumps: Updated Questions & Answers (November 2025)

When migrating content between Tableau servers, certain types of content may require special consideration or different tools for migration:

Tableau Prep Flows: These are specific to Tableau Prep and are not included in the standard content migration capabilities of the Tableau Content Migration Tool. Tableau Prep flows often require separate processes for migration due to their distinct setup and integration with data sources and workflows.

Published Data Sources and Workbooks: These can typically be migrated directly using the Tableau Content Migration Tool, which supports moving published data sources (both live connections and extracts) and workbooks without requiring additional tools.

The approach of using user filters to control access to data on profitability for Groups 2 and 3, combined with a calculated field that restricts the visibility of profit value to only Group 2, aligns with Tableau’s best practices for managing content permissions. This method ensures that each group sees only the data they are permitted to view, with Group 1 not seeing any profitability information, Group 2 seeing both profit and profit margin, and Group 3 seeing only the profit margin without the actual profit values. This setup can be achieved through Tableau Server’s permission capabilities, which allow for detailed control over what each user or group can see and interact with12.

To improve the performance of a Tableau workbook, it is best practice to streamline the data being used. This can be achieved by using filters to limit the data to only what is necessary for analysis, hiding fields that are not being used to reduce the complexity of the data model, and aggregating values to simplify the data and reduce the number of rows that need to be processed. These steps can help reduce the load on the server and improve the speed of the workbook.

The calculation {FIXED [Customer ID], [Region]: COUNTD([Order ID])} is the correct one to use for this scenario. This Level of Detail (LOD) expression will calculate the distinct count of orders for each customer within each region, which is then averaged per month. This approach ensures that the average number of orders per customer is accurately calculated for each region and then broken down by month, aligning with the client’s expectations.

The Explorer (can publish) site role in Tableau is designed for users who need to access, edit, and create visualizations based on published data sources, even when they are outside the company network. This role allows users to perform web editing and save their work, making it suitable for sales users who need these capabilities. It is also a cost-effective option as it does not require the full capabilities and associated costs of the Creator license.

To minimize the impact on users when reverting to a previous version of a published data source in Tableau Server, the consultant should use the built-in revision history feature. By selecting a previous version from the revision history and clicking ‘Restore’, the data source will revert to that version without the need for a full server backup restoration or manual recreation of the data source. This process is quick and has the least amount of disruption to users.

The Workbook Optimizer is a tool specifically designed to evaluate a workbook against performance best practices. It provides feedback on key design characteristics and offers concrete guidance on how to improve workbook performance. This tool is beneficial for both new and experienced Tableau users to ensure their dashboards are optimized for performance1.

Using an extract filter is an effective way to reduce the amount of data in a Tableau extract. Extract filters allow you to specify a subset of the data to include, which can significantly decrease the size of the extract by excluding unnecessary data. This is particularly useful when you only need partial data from a larger SQL Server data source.

Comprehensive and Detailed Explanation From Exact Extract:

Tableau allows certain calculations to be materialized in extracts, meaning they are precomputed and stored inside the .hyper file to improve performance.

According to Tableau’s extract documentation:

Materializable calculations must be compatible with the extract engine and must not depend on dynamic, view-based, or post-query logic.

Only row-level calculations and aggregation-level calculations without dependencies on runtime context can be materialized.

Tableau cannot materialize any calculation containing:

Table calculation functions

Functions requiring post-aggregation logic

View-dependent elements

Parameters that need runtime evaluation

Evaluation of the choices:

A. A row-level calculation — Correct

Row-level calculations operate on each record individually before aggregation.

Tableau documentation specifies that these calculations can be pushed down into the extract and materialized because they do not depend on the visualization or user interaction.

Examples include concatenation, arithmetic, string manipulation, and row-based logic such as:

[Sales] * [Quantity] or IF [Region] = 'West' THEN 1 END

These can be precomputed inside the extract, improving performance.

B. A calculation that contains table calculation functions — Not allowed

Table calculations (WINDOW_SUM, INDEX, RUNNING_SUM, RANK, etc.) depend on the table structure after aggregation and query execution.

Therefore, Tableau documentation states they cannot be materialized in extracts.

C. A calculation that contains parameters — Not allowed

Parameters are evaluated at runtime, meaning the user can change their value.

Because of this, Tableau cannot permanently compute and store such a calculation inside an extract.

D. A calculation that contains an aggregation — Generally not materialized

Aggregated calculations often depend on query context and cannot always be materialized.

Only simple, context-free aggregations might be materialized, but Tableau explicitly warns that aggregations are not guaranteed candidates for extract materialization.

Thus, this is not the best answer compared to row-level logic.

Conclusion

Only row-level calculations meet Tableau’s exact requirements for materialization in extracts.

References From Tableau Consultant Documentation

Tableau Extract documentation describing materializable calculation types.

Tableau guidance stating table calculations and parameter-dependent calculations cannot be materialized.

Extract optimization guidelines describing row-level logic as eligible for materialization.

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