Analytics-Con-301 Salesforce Certified Tableau Consultant Questions and Answers

To modify the Percent Contribution calculated field to use a Level of Detail (LOD) expression and accurately calculate the percentage contribution of each manufacturer to the total quantity, follow these steps:

Open the CC Workbook and Access the Worksheet:

Double-click on the CC workbook from the desktop to open it in Tableau Desktop.

Navigate to the Manufacturers worksheet by selecting its tab at the bottom of the window.

Modify the Percent Contribution Calculated Field:

Navigate to the Data pane and find the "Percent Contribution" calculated field.

Right-click on the "Percent Contribution" field and select 'Edit'.

Modify the formula to incorporate an LOD expression that calculates the total quantity across all manufacturers and the specific quantity per manufacturer:

{FIXED [Manufacturer]: SUM([Quantity])} / {SUM([Quantity])}Quantity])}

This formula uses {FIXED [Manufacturer]: SUM([Quantity])} to compute the total quantity contributed by each manufacturer, regardless of other dimensions in the view. The total quantity {SUM([Quantity])} calculates the grand total across all manufacturers. The division calculates the percentage contribution.

Click 'OK' to save the updated calculated field.

Enter Percentage for Newell:

With the updated "Percent Contribution" field, drag it onto the view to update the chart or table.

Identify the value corresponding to 'Newell' in the updated visualization.

Round this value to the nearest hundredth of a percent as required.

Enter this value into the "Newell % Contribution" parameter. To do this, locate the parameter in the Data pane or on the dashboard, right-click it, and choose 'Edit'. Enter the calculated percentage for Newell.

Save Your Changes:

From the File menu, click 'Save' to store all the modifications you have made to the workbook.

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.

To complete the Pareto chart in the "City Pareto" worksheet of your Tableau Desktop and add reference lines to illustrate how the data compares to the Pareto principle, follow these 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 City Pareto worksheet by selecting its tab at the bottom of the window.

Construct the Pareto Chart:

Ensure that sales data is aggregated by city. If not, drag the 'City' dimension to the Columns shelf and the 'Sales' measure to the Rows shelf.

Sort the sales data in descending order to properly align the cities according to their sales contribution.

To create a running total of sales, right-click on the 'Sales' measure on the Rows shelf, select 'Quick Table Calculation', and choose 'Running Total'.

Drag the 'Number of Records' field to the Rows shelf next to the Sales running total. Right-click on it, select 'Quick Table Calculation', and choose 'Running Total'. Set its calculation to 'Percent of Total' from the 'Edit Table Calculation' option to represent the percentage of cities.

Add Reference Lines for the Pareto Principle:

Click on the Analytics tab in the sidebar.

Drag a 'Reference Line' element and drop it onto the chart area.

Set the Reference Line for the Sales axis at 80% to represent the typical Pareto cutoff where 80% of effects come from 20% of causes.

Add another Reference Line on the axis representing the percentage of cities, set at 20%, to visually assess the Pareto principle.

Adjust the Appearance of the Chart:

Format the reference lines by right-clicking on them, selecting 'Edit', and choosing a distinct style or color to make them stand out.

Ensure the chart is clear and labels are appropriately adjusted for easy understanding of the data visualization.

Save Your Changes:

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

Given the need for up-to-date data on a daily basis and weekly revisions, the best approach is to use an incremental extract refresh daily to update the data source with new transactions. On Saturdays, when incorrect transactions are revised, a full extract refresh should be performed to incorporate all revisions and ensure the data’s accuracy. This strategy allows end users to have access to the most current data throughout the week while also accounting for any necessary corrections12.

Moving calculations to the data layer and materializing them in the extract can significantly improve the performance of reports in Tableau. The calculation ZN([Sales])*(1 - ZN([Discount])) is a basic calculation that can be easily computed in advance and stored in the extract, speeding up future queries. This type of calculation is less complex than table calculations or LOD expressions, which are better suited for dynamic analysis and may not benefit as much from materialization12.

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 most efficient way to limit report visibility to specific user groups on Tableau Server is to manage permissions directly on the server. By granting access to the report only to the sales and marketing user groups, the client ensures that only members of these groups can view the report. This method is straightforward and does not require the additional steps involved in setting up row-level security or user filters.

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.

The discrepancy between the count of workbooks in the Connected Workbooks tab and the lineage count in Tableau Catalog can occur because of user permissions. In Tableau Data Management, the visibility of connected workbooks is subject to the permissions set by administrators. If a user does not have permission to view certain workbooks, they will not see them listed in the Connected Workbooks tab, even though these workbooks are part of the data source’s lineage and are counted in Tableau Catalog.

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.

When “Apply to Nested Projects” is checked in Tableau Server, the permission rules set at the top-level project are enforced for all assets in the project and all nested projects. This means that any custom access previously granted to nested projects will be overridden, and the permissions will revert to those defined at the top-level project. This action ensures consistent application of the data governance strategy across all divisions.

When field agents are unable to access workbooks due to issues with security token-based credentials, the most immediate and least disruptive solution is to renew the security token. This can be done through the Data Connection settings on Tableau Server. Renewing the token will restore access for the field agents without requiring them to take any action or affecting other users.

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.

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.

Comprehensive and Detailed Explanation From Exact Extract:

According to Tableau’s Performance Optimization guidance, rendering time becomes the largest bottleneck when excessive marks, dense data, or overly complex visualizations appear on worksheets. Rendering is the last stage in the Tableau Order of Operations and is directly affected by how many marks must be drawn and how visually complex each view is.

Tableau’s performance recommendations explain:

When a performance recording shows that Rendering is the slowest step, the most effective improvement is to reduce the number of marks (records) in the view.

Rendering time is determined by the number of marks, shapes, headers, labels, and visual elements Tableau must draw.

Reducing the amount of data displayed on each worksheet is the most impactful change when rendering is the dominant delay.

Option B directly aligns with this: updating worksheets to reduce the number of records displayed lowers the number of marks, reduces visual density, and improves rendering speed.

Option A is not effective because changing dashboard size does not reduce the number of marks.

Option C would degrade performance because live connections are typically slower than extracts.

Option D improves data preparation and may reduce extract load times, but it does not directly address rendering unless the unused data was contributing to marks in the view. The question indicates the bottleneck is specifically rendering, so reducing marks is the most appropriate action.

Therefore, the most effective solution to reduce rendering time is to reduce the number of records (marks) displayed on worksheets.

Tableau Performance Recording guidance describing rendering as the slowest stage when too many marks are present.

Tableau Performance Checklist recommending reducing the number of marks in views to improve rendering.

Tableau Desktop help sections on best practices for improving visualization performance when rendering dominates.

When dashboards are published to Tableau Cloud, the function USERNAME() returns the user’s Tableau Cloud username, which is the email address associated with their Tableau Cloud account.

Tableau documentation states:

On Tableau Cloud, the value returned by USERNAME() is always the user’s email address.

Row-Level Security (RLS) is typically implemented using a comparison of USERNAME() to an email field in the data source.

For secure filtering, the field compared to USERNAME() must match the authentication identity exactly.

Looking at the provided table:

“Abbreviated Name” contains short custom codes like “SMiller,” which do not match Tableau Cloud usernames.

“Lower Case Name” contains names like “sean miller,” which also do not match Tableau Cloud usernames.

“Email” contains the full email address for each user, such as “Sean.Miller@superstore.com,” which is the only field that corresponds to what USERNAME() returns in Tableau Cloud.

Therefore, the correct field to reference is Email.

Tableau Cloud authentication documentation stating USERNAME() returns the user’s email address.

Row-Level Security setup guidance recommending the comparison USERNAME() = [Email Field].

Tableau security practices indicating only the email column will match USERNAME() values on Tableau Cloud.

To flag orders with sales higher than the regional average, the correct calculated field would compare the sum of sales for each order against the average sales of all orders within the same region:

Correct Formula: { FIXED [Order ID] : SUM([Sales]) } > { FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) }

This calculation uses a Level of Detail (LOD) expression:

The left part of the formula { FIXED [Order ID] : SUM([Sales]) } calculates the total sales for each individual order.

The right part { FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) } calculates the average sales per order within each region.

The > operator is used to compare these two values to determine if the sales for each order exceed the regional average.

References

This formula utilizes Tableau's LOD expressions to perform complex comparisons across different dimensions of the data, as explained in Tableau's official training materials on LOD calculations.