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

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.

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.

To ensure that users automatically see Tableau views of regional sales filtered by customer as they sign into Salesforce, embedding the views directly into Salesforce is most effective:

Embedding Views: Tableau provides capabilities to embed its dashboards into web applications such as Salesforce. This approach ensures that the visualization is part of the Salesforce user interface, enhancing user experience by not requiring users to navigate away from Salesforce to view the data.

Implement this by using Tableau's embedding code, which can be generated from the Tableau Server for each specific view. Place this embed code into the Salesforce Visualforce pages or use Salesforce Canvas to integrate these views seamlessly.

This setup allows the Tableau views to inherit user credentials from Salesforce, enabling personalized data visualization based on the user's access rights and region, directly aligned with their Salesforce login session.

References

The embedding technique is documented in both Tableau's and Salesforce's official integration guides, which provide step-by-step instructions on embedding Tableau views into Salesforce platforms.

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.

To shape the data in the "US Population Estimates" data source and enter the total number of records into the "Total Records" parameter in Tableau Desktop, 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 Population worksheet by selecting its tab at the bottom of the window.

Format and Shape the Data:

Ensure the data types match those specified in the requirements: Sex, Origin, Race as strings; Year, Age, Population as whole numbers.

To verify or change the data type, click on the dropdown arrow next to each field name in the Data pane and select "Change Data Type" if necessary.

Calculate Total Number of Records:

Create a new calculated field named "Total Records". To do this, right-click in the Data pane and select "Create Calculated Field".

Enter the formula COUNT([Record ID]) or SUM([Number of Records]) depending on how the data source identifies each row uniquely.

Drag this new calculated field onto the worksheet to display the total number of records.

Enter the Value into the Total Records Parameter:

Locate the "Total Records" parameter in the Data pane. Right-click on the parameter and select "Edit".

Manually enter the number displayed from the calculated field into the parameter, ensuring accuracy to meet the data shaping requirement.

Save Your Changes:

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

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.

Comprehensive and Detailed Explanation From Exact Extract:

Quick filters are one of the most expensive features in Tableau because they require queries to populate value lists and dynamic recalculations when filters change.

According to Tableau performance documentation:

1. Add an “Apply” Button

This prevents Tableau from re-running queries every time the user selects a filter value.

Queries are executed once when the user presses Apply.

This is a documented best practice for filter-heavy dashboards.

2. Replace Quick Filters with Filter Actions

Filter actions are far more efficient because:

They leverage the existing view context

They do not require separate filter UI queries

They avoid the overhead of quick filter value lists

Tableau recommends using filter actions instead of multiple quick filters for better performance.

Why the other options are incorrect:

B. Add filters to Context: Context filters make downstream filters faster, but do not reduce quick filter processing cost; they can even increase extract size and slow down the dashboard.

C. Only Relevant Values: This actually slows performance because Tableau must re-evaluate the entire data set to determine relevancy every time filters update.

Thus, A and D are the correct performance-improvement approaches.

Tableau Performance Checklist recommending Apply button for multi-select filters.

Performance documentation advising the use of Filter Actions over multiple quick filters.

Filtering best practices explaining the cost of Only Relevant Values.

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.

Comprehensive and Detailed Explanation From Exact Extract:

Tableau Cloud does not natively contain a Sankey diagram in Show Me. Such advanced charts often require:

A custom extension

Or specialized templates built into Tableau Exchange

A sandboxed Viz Extension allows users to embed specialized visualization components (like Sankey diagrams) securely in Tableau Cloud. These extensions are designed for advanced chart types that are not available natively.

Accelerators provide prebuilt dashboards but are not intended for custom visual types such as Sankey.

Connectors relate to connecting to data sources, not visualization.

Show Me does not include a Sankey option.

Therefore, downloading a sandboxed Viz Extension is the correct approach.

Viz Extensions documentation explaining support for custom charts, including Sankey.

Tableau Exchange listing providing sandboxed visualization extensions for non-native chart types.

Show Me panel documentation showing Sankey is not an included chart type.

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.

Comprehensive and Detailed Explanation From Exact Extract:

The requirement is:

Compute total sales per customer, not per transaction.

Then compute the average of those customer totals, grouped by region.

Tableau documentation states that FIXED LOD expressions are used to calculate values at a specific level of granularity regardless of the view.

To solve the business need:

Step 1:

Calculate total customer sales for each Customer ID within each Region:

{ FIXED [Customer ID], [Region] : SUM([Sales Amount]) }

This produces one number per customer per region.

Step 2:

Compute the average of those totals:

AVG( { FIXED [Customer ID], [Region] : SUM([Sales Amount]) } )

This yields:

Average total customer sales by region

This is exactly option C.

Why the other options are incorrect:

A. EXCLUDE Region: Would combine regions and incorrectly calculate overall totals.

B. FIXED Region: AVG(Sales Amount): Computes average of line-level sales, not customer totals.

D. FIXED Customer ID + Region: AVG(Sales Amount): Averages individual transactions, not customer totals.

Only option C matches the required two-step logic.

LOD Expressions: FIXED for computing customer-level aggregates.

Nested LOD usage for first calculating customer totals, then averaging them at a higher level.

Tableau guidance: SUM inside FIXED for per-customer totals, AVG outside for averaging customers.

For implementing row-level security (RLS) without the Tableau Data Management Add-on, the best approach is to integrate user filters into the published data source:

Creating User Filters on Published Data Source: This method involves defining user filters that apply directly to the data source before it is published to the Tableau Server. This ensures that any workbook or view leveraging this data source inherently respects the row-level security settings.

To implement this, create a calculated field in Tableau that defines the security logic, typically using a formula that references user functions (like USERNAME() or ISMEMBEROF()). Drag this field to the Filters shelf and configure it to match the security rules (who can see what data).

Once configured, publish the data source to Tableau Server with these filters in place. This approach centralizes security management, making it easier to maintain and update security policies as they are applied universally to all workbooks using this data source.

This strategy is safe as it reduces the risk of accidental data exposure through individual workbook misconfiguration and simplifies long-term maintenance of security policies.

References

This method follows Tableau’s best practices for implementing row-level security as detailed in Tableau’s security management resources. It ensures robust, maintainable security measures that scale with organizational needs without requiring additional add-ons.

For a performant live connection in Tableau Cloud, especially when dealing with large datasets that preclude the use of extracts, relating the tables in Tableau Desktop is the recommended approach. This method allows for flexibility in how the data is queried and can improve performance by leveraging Tableau’s relationships feature, which optimizes queries for the underlying database.

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.

The correct calculation for classifying profit margins into categories based on specified thresholds involves the use of conditional statements that check ranges in a logical order:

Highly Profitable Classification: The first condition checks if the profit margin is 50% or more. This must use the ">=" operator to include exactly 50% as "Highly Profitable".

Profitable Classification: The next condition checks if the profit margin is between 0% and 50%. Since any value falling at or above 50% is already classified, this condition only needs to check for values greater than or equal to 0%.

Unprofitable Classification: The final condition captures any remaining scenarios, which would only be values less than 0%.

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 DATEPART('iso-weekday', [Order Date])=1 or DATEPART('iso-weekday', [Order Date])=7 is recommended because the ISO standard considers Monday as the first day of the week (1) and Sunday as the last day (7). This calculation will correctly identify Saturdays and Sundays regardless of the locale settings of the workbook’s data source, ensuring that the report includes these days as specified by the client.

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.

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.

To improve the performance of a Tableau dashboard while maintaining accurate counts, particularly when dealing with unknown or NULL BrowserIDs in the data tables, the following steps are recommended:

Modify the Browser Table: Add a new row to the Browser Table labeled "Unknown," assigning it a unique BrowserID, e.g., 0 or 4.

Update the Site Visitor Table: Replace all NULL BrowserID entries with the BrowserID assigned to the "Unknown" entry. This ensures every record in the Site Visitor Table has a valid BrowserID that corresponds to an entry in the Browser Table.

Change Referential Integrity Setting: Change the Referential Integrity setting from "Some Records Match" to "All Records Match." This change assumes all records in the primary table have corresponding records in the secondary table, which improves query performance by allowing Tableau to make optimizations based on this assumption.

When a filter is applied directly to the view, it can affect the calculation of percentages in a histogram because it changes the underlying data that the quick table calculation is based on. To avoid this, adding the [Profit] filter to the context will maintain the original calculation of percent shares while filtering out bins with less than a 15% share. This is because context filters are applied before any other calculations, so the percent shares calculated will be based on the context-filtered data, thus preserving the integrity of the original percent labels.

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.