Analytics-Con-301 Sample Questions 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.

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.

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 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 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.

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.

To compute the required calculations and update the worksheet in Tableau Desktop, follow these steps:

Compute Average Sales by Segment for Each Category and Year:

Open the CC workbook and navigate to the Categorical Sales worksheet.

Drag the 'Sales' field to the Rows shelf if it's not already there.

Drag the 'Segment' field to the Rows shelf as well, placing it next to 'Category' and 'Year'.

Right-click on the 'Sales' field in the Rows shelf and select 'Quick Table Calculation' > 'Average'. This will compute the average sales for each segment within each category and year.

Create a Calculation for Year-over-Year Percentage Change:

Right-click in the data pane and select 'Create Calculated Field'.

Name the calculated field something descriptive, e.g., "YoY Sales Change".

Enter the formula to calculate the year-over-year percentage change:

(ZN(SUM([Sales])) - LOOKUP(ZN(SUM([Sales])), -1)) / ABS(LOOKUP(ZN(SUM([Sales])), -1))

Click 'OK' to save the calculated field.

Replace the Original Measure with the Year-over-Year Percentage Change in the Crosstab:

Remove the original 'Sales' measure from the view by dragging it off the Rows shelf.

Drag the newly created "YoY Sales Change" calculated field to the Rows shelf where the 'Sales' field was originally.

Format the "YoY Sales Change" field to display as a percentage. Right-click on the field in the Rows shelf, select 'Format', and adjust the number format to percentage.

Save Your Changes:

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

The best approach to ensure performance while providing data for only the last day (yesterday) in the dataset is to use a calculated field that filters the data to include only yesterday's date:

Filter on calculation [Ship Date]=TODAY()-1: This calculated field dynamically computes yesterday's date by subtracting one day from today’s date. This approach ensures that each day, only the data for the previous day is loaded, which keeps the volume of data minimal and improves performance.

Dynamic Date Calculation: The use of TODAY()-1 ensures the filter remains up-to-date with the changing dates, without the need for manual updates, providing accuracy and timeliness in the dashboard.

This approach is efficient because it avoids the overhead of processing the entire dataset and focuses only on the relevant day’s data. It also aligns with Tableau’s capabilities for creating dynamic filters using date functions, as highlighted in the Tableau help documentation on date calculations and filters.

References

This solution utilizes Tableau’s built-in date functions and dynamic calculations to optimize performance, as recommended in Tableau’s performance optimization resources and date calculation guidelines.

Comprehensive and Detailed Explanation From Exact Extract:

Tableau’s row-level security (RLS) is applied at the data source level so that all users who connect to the data source—whether through Tableau Desktop, Server, or Cloud—see only the data they are permitted to see.

According to Tableau documentation:

A Data Source Filter is the correct method for enforcing consistent row-level security for all users.

When a Data Source Filter is applied before publishing, it becomes part of the data source’s metadata and is applied every time any user connects to the published source.

This ensures that users creating new workbooks in Tableau Desktop are governed by the same RLS as users viewing published dashboards.

Context filters and extract filters do not provide secure RLS:

A Context Filter only applies inside the workbook where it is created. It does not enforce security in Tableau Desktop when the data source is reused.

An Extract Filter physically removes rows from the extract but does not enforce role-based filtering or dynamic RLS.

“Apply Filter to All Using Related Data Sources” affects workbook behavior, not published data source security.

A Data Source Filter applied prior to publishing is Tableau’s documented approach for secure, reusable row-level security.

Row-Level Security implementation guidance describing Data Source Filters as the foundation of secure RLS.

Tableau Server publishing workflow indicating that Data Source Filters travel with the published source.

Documentation on why Context and Extract Filters do not enforce user-dependent row-level security.

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:

To combine Excel and SQL Server data in the same logical data model, Tableau offers two supported capabilities:

Relationships

Recommended modern method for combining tables from multiple sources.

Supports cross-database relationships between Excel and SQL Server.

Maintains separate physical layers but integrates data at query time.

Cross-Database Joins

Allows joining data from different databases in the physical layer.

Fully supported for Excel + MS SQL Server.

Useful when granular row-level merging is needed.

Why the other options are incorrect:

C. Data Blending

Legacy feature, used only when no direct combination is possible.

Tableau recommends relationships instead.

Produces separate queries and may lose row-level detail.

D. Union

Requires tables to have equivalent structure.

Cannot union Excel with SQL Server unless identical column structure exists.

Not appropriate for most mixed-source reporting.

Therefore, the correct techniques are Relationships and Cross-Database Joins.

Tableau data modeling documentation recommending Relationships for multi-source modeling.

Cross-database join support list including Excel + SQL Server.

Comprehensive and Detailed Explanation From Exact Extract:

Tableau’s documentation on Parameter Actions states that they allow users to interactively update a parameter directly from the view—without opening the parameter control box.

To “interactively set a reference date” that drives a table calculation:

A parameter must hold that reference date.

A parameter action allows clicking or selecting a mark in the view to update the parameter value.

Table calculations can then reference that parameter to change their computation dynamically.

Filter actions modify which data is shown, not a reference date.

Running actions change sheets, not computation parameters.

Highlight actions visually accent marks but do not set values.

Thus, parameter actions are the only mechanism that meets the requirement.

Parameter Actions overview describing interactive parameter updates.

Use cases where parameter actions drive table calculations.

Action type comparison showing that only parameter actions update a computation value.

Tableau’s official in-place upgrade steps for single-node Tableau Server require:

Disable scheduled tasks (to prevent extract failures during upgrade).

Run the upgrade script for the new version.

Validate and test the server after upgrade.

The critical detail:

An in-place upgrade means upgrading the existing production environment directly, not installing in a separate environment or cloning VMs.

Option B matches exactly the documented operational steps for an in-place upgrade.

Why the other options are incorrect:

A: Uninstalling Tableau Server is not part of an in-place upgrade workflow.

C: This describes a migration or test upgrade in a separate environment, not an in-place upgrade.

D: VM cloning and restoring is not required for in-place upgrades and is not part of Tableau’s official procedure.

Only B represents the correct in-place upgrade sequence.

Tableau Server in-place upgrade instructions: disable tasks → run upgrade script → test.

Single-node upgrade guidelines noting that uninstalling is not required.

Tableau Server administration materials clarifying the difference between in-place upgrade and sandbox testing.

Comprehensive and Detailed Explanation From Exact Extract:

View Acceleration is a Tableau Server and Tableau Cloud feature that speeds up slow-loading views by precomputing their queries in the background.

According to Tableau’s server and cloud performance documentation:

When View Acceleration is enabled for a view, Tableau runs the queries behind that view on a background process and stores the results in memory.

When an end user later opens the view, Tableau can serve the precomputed results immediately, rather than running the potentially long-running queries at that moment.

This improves initial load time significantly for views that are slow because of heavy queries.

It does not only work with extract-based data sources (it can also help with many live connections), so option A is too limited.

It does not change the client-side rendering engine, so option C is incorrect.

It is not specific to transient functions but to any view where the query is expensive, so option D is not accurate.

Therefore, the correct description is that View Acceleration precompiles and fetches workbook data in a background process, which matches option B.

Tableau Server and Tableau Cloud help describing View Acceleration as precomputing and caching view results using background processes.

Performance tuning guidance recommending View Acceleration for views with slow query execution.

To transform the Orderld field in Tableau Prep, the Split option is the most efficient and straightforward method. Here’s how you can apply it:

In Tableau Prep, drag your dataset into the flow.

Click on the Orderld field in the workspace to select it.

Look for the option in the toolbar that says "Split" and select it.

Choose "Automatic Split" if the delimiters (such as hyphens) are consistent; Tableau Prep should automatically detect the hyphen as the delimiter and split the Orderld into multiple new fields.

The dataset should now show new columns: one for the country code (CA, FR, US), one for the year (2017), and one for the order number (152156, 152157, etc.).

The Split option works effectively here because it automatically identifies and uses the hyphen as the delimiter to divide the original Orderld into the desired components without manual specification of conditions or writing any formulas.

References

This procedure is based on the standard functionalities provided in Tableau Prep for splitting a field into multiple columns based on a delimiter, as described in the Tableau Prep user guide.

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.