To record the performance of the Property Transactions dashboard in the NYC Property Transactions.twbx workbook and analyze it using a bar chart, follow these detailed steps:

Open the NYC Property Transactions Workbook :

From the desktop, double-click the NYC Property Transactions.twbx workbook to open it in Tableau Desktop.

Start Performance Recording :

Before doing anything else, navigate to the ' Help ' menu in Tableau Desktop.

Select ' Settings and Performance ' , then choose ' Start Performance Recording ' .

Open the Property Transactions Dashboard and Reset Filters :

Navigate to the Property Transactions dashboard within the workbook.

Reset all filters to show all values. This usually involves selecting the dropdown on each filter and choosing ' All ' or using a ' Reset ' button if available.

Stop the Performance Recording :

Go back to the ' Help ' menu.

Choose ' Settings and Performance ' , then select ' Stop Performance Recording ' .

Tableau will automatically open a new tab displaying the performance recording results.

Save the Performance Recording :

In the performance recording results tab, go to the ' File ' menu.

Click ' Save As ' and navigate to the C:\CC\Data\ directory.

Save the file, ensuring it is stored in the desired location.

Create a New Worksheet for Performance Analysis :

Return to the NYC Property Transactions workbook and create a new worksheet by clicking on the ' New Worksheet ' icon.

Drag the ' Command Name ' field to the Columns shelf.

Drag the ' Elapsed Time ' field to the Rows shelf.

Ensure that the ' Worksheet ' field is also included in the analysis to break down the time by individual sheets within the dashboard.

Choose ' Bar Chart ' from the ' Show Me ' options to display the data as a bar chart.

Customize and Finalize the Bar Chart :

Adjust the axes and labels to clearly display the information.

Format the chart to enhance readability, applying color coding or sorting as needed to emphasize sheets with longer load times.

Save Your Work :

Once the new worksheet and the performance recording are complete, ensure all work is saved.

Navigate to the ' File ' menu and click ' Save ' , confirming that changes are stored in the workbook.

To add a line showing the cumulative percentage of sales contributed by each product to the incremental sales in the Incremental worksheet of your Tableau Desktop, follow these detailed steps:

Open the CC Workbook and Access the Worksheet :

From the desktop, double-click on the CC workbook to open it in Tableau Desktop.

Navigate to the Incremental worksheet by clicking on its tab at the bottom of the window.

Calculate Cumulative Sales Percentage :

Create a new calculated field to compute the cumulative percentage of sales. Right-click in the Data pane and select ' Create Calculated Field ' .

Name this field " Cumulative Sales Percentage " .

Enter the following formula to calculate the running sum of sales as a percentage of the total sales:

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

Click ' OK ' to save the calculated field.

Add the Cumulative Sales Percentage Line to the Chart :

Drag the " Cumulative Sales Percentage " field to the Rows shelf, placing it next to the existing Sales measure.

Ensure that the cumulative line appears as a continuous line. Right-click on the " Cumulative Sales Percentage " field on the Rows shelf, select ' Change Chart Type ' , and choose ' Line ' .

Adjust the axis to synchronize or dual-axis if necessary. Right-click on the axis of the " Cumulative Sales Percentage " and select ' Synchronize Axis ' if it ' s on a dual-axis setup.

Format the Cumulative Sales Percentage Line :

Click on the " Cumulative Sales Percentage " line in the visualization.

Navigate to the ' Format ' pane to adjust the line style, thickness, and color to make it distinct from other data in the chart.

Save Your Changes :

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

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.

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 performance 1 .

The Tableau Catalog is part of the Tableau Data Management Add-on and is designed to help users understand the data they are using within Tableau. It provides a comprehensive view of all the data assets in Tableau Server or Tableau Online, including databases, tables, and fields. It can help identify issues such as data quality, data lineage, and impact analysis. In this case, where different departments are getting different answers to the same business questions, the Tableau Catalog can be used to track down inconsistencies and ensure that everyone is working from the same, reliable data source.

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 processed 1 2 .

Comprehensive and Detailed Explanation From Exact Extract:

In Tableau Performance Recording, “Executing Query” refers to the amount of time Tableau spends executing the SQL or hyper query generated by the workbook. When an extract is used, the query is executed against the .hyper extract , not the original database.

Tableau documentation identifies several causes of slow query execution within extracts, including:

Nested row-level calculations

Complex logic in calculated fields

Multiple Levels of Detail (LOD) expressions

Non-optimized expressions that force Tableau to compute additional temporary tables

These directly increase query complexity and cause longer “Executing Query” durations.

Therefore, optimizing the query requires simplifying or replacing:

Nested calculations

Unnecessary LOD expressions

Complex expressions that increase the workload on the extract engine

Option A is incorrect because the number of VizQL processes affects concurrency, not query execution time.

Option B is partially relevant, but dashboard filters affect the overall workload, not the specific query complexity. If the performance recording shows “Executing Query” as the slow section, the query itself (not the filter UI layer) is the problem.

Option D does not apply because extracts use the hyper engine, not the underlying database. Optimizing the original database structure does not change the extract query execution time.

Thus, the consultant should focus on simplifying nested calculations and LODs to reduce extract query complexity.

Tableau Performance Recording guide describing “Executing Query” as dependent on calculation complexity.

Tableau extract engine documentation explaining that nested logic, multiple LODs, and granular calculations generate slower extract queries.

Best practices recommending simplification of calculated fields to improve extract query performance.

Comprehensive and Detailed Explanation From Exact Extract:

In Tableau Cloud, database-level RLS can be used only with live connections because:

Tableau Cloud issues SQL queries using the logged-in user ' s identity.

Extracts break RLS because data is pulled out of the database and stored in Tableau’s hyper file.

To leverage existing RLS rules, Tableau must query the database directly for the user.

Therefore:

Requirement 1:

The Tableau Cloud username (email) must exist in the database

so that the database can enforce RLS using the authenticated identity.

Requirement 2:

Only live data connections support database-native RLS.

Extracts bypass database security and therefore cannot use RLS defined in the database.

Option D is incorrect because RLS is enforced in the database , not configured in Tableau Cloud.

Option B is incorrect because extracts cannot use database RLS.

Thus, correct answers are A and C .

Tableau Cloud live connection security requirements.

Database RLS documentation requiring matching database user identities.

Explanation that extracts bypass database permission systems.

Comprehensive and Detailed Explanation From Exact Extract:

Tableau Catalog provides multiple features for communicating data quality and freshness.

Data Quality Warnings (DQWs) are part of Catalog’s metadata management system and are specifically designed to inform users about data issues, including when data is stale .

There are two visibility levels:

1. Standard Visibility Data Quality Warning

Appears subtly in metadata panels.

Intended for non-critical issues.

Does not guarantee the message will be seen by dashboard viewers.

2. High Visibility Data Quality Warning

Designed for urgent, critical, and highly visible alerts .

Displays a prominent warning indicator directly on connected dashboards, data sources, and workbooks .

Tableau documentation states high-visibility warnings are used when users must be alerted , such as:

Stale data

Incomplete refreshes

Data outages

Because the question emphasizes:

“important to alert dashboard viewers when data has not been refreshed”

A standard warning is not strong enough , but a High Visibility Data Quality Warning is explicitly designed for this scenario.

Evaluation of the choices:

A. Standard Visibility Data Quality Warning — Not sufficient

It does not force dashboard users to notice the warning.

B. High Visibility Data Quality Warning — Correct

This option is specifically meant to notify users of critical freshness issues , making it the perfect match for the requirement.

C. Certified Data Source — Incorrect

Certification communicates trustworthiness, not freshness or alerts.

D. Lineage — Incorrect

Lineage shows data relationships and dependencies, not refresh warnings.

Conclusion

To alert viewers about stale data in hourly-refreshed dashboards, the consultant must use a High Visibility Data Quality Warning .

References From Tableau Catalog Documentation

Description of Data Quality Warnings and their visibility levels.

Definition of High Visibility DQWs as critical alerts shown to dashboard viewers.

Catalog guidelines for stale data detection and communication.

To build efficient extracts from large data sources, it is crucial to minimize the load and optimize the performance of the extracts:

A. Keep only the data required for analysis by using extract filters : This best practice involves using filters to reduce the volume of data extracted, thus focusing only on the data necessary for analysis.

B. Use aggregate data for visible dimensions, whenever possible : Aggregating data at the time of extraction reduces the granularity of the data, which can significantly improve performance and reduce the size of the extract.

E. Hide all unused fields : Removing fields that are not needed for analysis from the extract reduces the complexity and size of the data model, which in turn enhances performance and speeds up load times.

These practices are endorsed in Tableau’s official documentation and training sessions as effective ways to enhance the performance of Tableau extracts and optimize dashboard responsiveness.