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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.

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 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.

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 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.

For a client who needs a daily visualization of widget inventory but cannot modify the data warehouse structure, the best approach is to use Tableau Prep to add new rows. Tableau Prep can be used to manipulate the existing dataset by adding missing date entries and appropriately adjusting inventory counts based on available data. This allows the creation of a complete daily snapshot for visualization without needing changes to the underlying database structure.

Tableau’s built-in geocoding supports only the following geographic roles:

Country

State / Province

County

City

Postal Code

Airport

Area codes

Congressional districts, etc.

Tableau does NOT natively geocode street-level addresses.

When a dataset contains street address fields, Tableau will not recognize them as geographic fields because:

Street Address 1 and Street Address 2 are not valid Tableau geographic roles.

Tableau cannot automatically translate street text (like “101 Random Place Rd”) into latitude/longitude.

Therefore, to plot street-level points on a map, Tableau requires:

Custom geocoding OR latitude/longitude fields.

Tableau documents that for exact address locations:

You must either supply latitude and longitude for each address, or

Use custom geocoding, where the user uploads a .csv with addresses matched to coordinates.

Why the other answer choices are incorrect:

B. Change the data type to Geographic role

Street address fields cannot be assigned a geographic role. Tableau will reject them or not map them.

C. Establish a hierarchy

Hierarchies help with drilldown but do not generate geographic coordinates for street addresses.

D. Add a map layer

Map layers allow visual overlays but cannot generate geocoding for address fields.

The only correct way to map street addresses is to custom geocode (or supply lat/long), which matches option A.

Tableau Geocoding documentation stating street-level addresses are not natively supported.

Custom Geocoding instructions for mapping exact address points.

Mapping best practices stating that lat/long or custom geocode files are required for street accuracy.

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.

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.