Salesforce Analytics-Con-301 Salesforce Certified Tableau Consultant Exam Practice Test

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 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 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 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 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.

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.

When migrating content between Tableau servers, certain types of content may require special consideration or different tools for migration:

Tableau Prep Flows: These are specific to Tableau Prep and are not included in the standard content migration capabilities of the Tableau Content Migration Tool. Tableau Prep flows often require separate processes for migration due to their distinct setup and integration with data sources and workflows.

Published Data Sources and Workbooks: These can typically be migrated directly using the Tableau Content Migration Tool, which supports moving published data sources (both live connections and extracts) and workbooks without requiring additional tools.

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.

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 RLS entitlement design patterns include:

1. Deepest Granularity Method

Requires one unique role ↔ one unique lowest-level value pairing.

Fails when the dataset contains duplicate lowest-level values (e.g., multiple “East” sub-regions across different regions).

Cannot operate correctly when some lowest-level values are NULL.

Thus, the deepest granularity method is not valid here.

2. Sparse Entitlements Method

Tableau documentation states:

Sparse entitlements define RLS at each level of the hierarchy instead of only at the lowest level.

This method supports duplicate lowest-level values.

Handles scenarios where some levels are NULL because higher-level entitlements (e.g. Region = AMER) can still correctly apply.

More flexible for hierarchical geographic structures (Region → Sub-Region → Country, etc.).

Given the client’s dataset:

Multiple “East” and “West” sub-regions

Some “Null” sub-regions

Hierarchical levels present

Sparse entitlements is the only correct and supported choice.

Why the incorrect options are wrong:

A & B — Deepest Granularity

Deepest granularity fails when the lowest-level values are not unique.

It cannot handle NULL values at the lowest tier.

Performance is not superior in this scenario.

D — Sparse because it is most performant

Performance is not the defining advantage.

Flexibility and ability to handle duplicate lowest-level values is.

Thus, C is the correct statement.

RLS entitlement design patterns: deepest vs. sparse entitlements.

Rules requiring unique lowest-level identifiers for deepest granularity.

Guidance stating sparse entitlements should be used when duplicates or NULL values exist in hierarchical structures.

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.

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.

When a client republishes a dashboard after making changes and users report broken bookmarks, the likely causes include:

The dashboard was published to a different project: Changing the project location alters the URL path, causing bookmarks to point to a now non-existent dashboard location.

The dashboard was published with a new name: Altering the dashboard's name changes its URL, resulting in broken bookmarks as the previous URL no longer leads to the intended dashboard.

Comprehensive and Detailed Explanation From Exact Extract:

Tableau performance documentation explains that rendering speed is strongly affected by the number of marks that Tableau must draw. Each dimension placed on the Detail shelf increases the granularity of the view and increases the number of marks in the visualization.

Removing unnecessary dimensions:

Reduces the number of marks

Reduces rendering time

Decreases memory and CPU usage

Improves interactive performance

Option A (Show Relevant Values) can slow performance because Tableau must dynamically calculate relevancy each time filters change.

Option C is incorrect because COUNTD is one of the slowest aggregate functions in Tableau and does not speed visualization.

Option D is incorrect because adding more sheets increases dashboard load time and rendering workload.

Removing unnecessary fields from Detail is a documented best practice for improving visualization speed.

Tableau Performance Checklist recommending reducing marks and removing unnecessary dimensions.

Rendering optimization guidance explaining how dimensions on Detail expand mark counts.

Best practices discouraging overuse of COUNTD.

Comprehensive and Detailed Explanation From Exact Extract:

When optimizing performance with live connections, Tableau documentation emphasizes minimizing the workload passed to the database. Key principles include:

Databases resolve numeric and Boolean fields significantly faster than strings and dates.

Using simpler data types reduces query parsing time and improves join and filter performance.

This is a standard Tableau recommendation for live query optimization.

Why the other answers are incorrect:

B: Table calculations add workload on Tableau, but live performance depends on database efficiency; granular table calc processing worsens performance.

C: Custom SQL often hurts performance because it disables query optimization, increases load times, and prevents Tableau from generating efficient queries.

D: Compute Calculations Now applies only to extracts, and has no effect on live connections.

Thus, the documented performance best practice for live sources is to use numbers and Booleans instead of strings and dates.

Live connection optimization guidance: prefer numeric and Boolean fields over strings/dates.

Best practices cautioning against Custom SQL for performance.

Documentation stating Compute Calculations Now applies only to extracts.

Comprehensive and Detailed Explanation From Exact Extract:

The dataset contains timestamps for each individual user interaction. Growth in user adoption over 6 months means the number of rows has expanded significantly. Tableau’s performance documentation states that large row-level datasets can cause performance degradation even when using extracts, especially when:

The visualization is aggregated to a higher level (such as daily), and

The underlying extract still contains much more granular data than needed.

Tableau recommends pre-aggregating data before it reaches Tableau Desktop, which reduces extract size, memory use, and query time. This improves performance without changing what the visualization displays.

Option D uses Tableau Prep, which is included with Tableau Creator licensing and therefore incurs no additional cost. Tableau Prep can aggregate raw timestamp data into daily totals per product feature, which matches the visualization’s actual granularity. This results in:

A dramatically smaller extract

Faster queries

No change to how the dashboard looks or functions

Option A would remove product features from the visualization, altering the dashboard content and reducing insight, which does not meet the requirement of minimal impact.

Option B requires purchasing an external ETL tool, which violates the requirement of no additional cost.

Option C reduces the number of extract refreshes but does not improve dashboard performance; the data would remain equally granular and equally slow.

Therefore, Tableau Prep aggregation is the correct solution that improves performance while maintaining the same visualization and incurring no additional cost.

Tableau performance guidelines recommending pre-aggregation of highly granular datasets.

Tableau Prep documentation stating it can be used to aggregate data before creation of extracts.

Tableau’s extract optimization guidance describing how reducing row counts improves query and visualization performance.

Comprehensive and Detailed Explanation From Exact Extract:

The client wants to always show the most recent day present in the data, not today’s date. The dataset is updated intermittently, meaning some days may have no new rows. Tableau documentation states that:

Using TODAY() recalculates on every query and prevents effective caching because Tableau must compute the current date for each refresh.

Using Relative Date Filters like “Last 1 day” also prevents caching because Tableau evaluates relative conditions each time the workbook loads.

Quick Filters also break caching and decrease performance because they require interactive evaluation on each render.

A FIXED LOD calculation allows Tableau to compute the maximum date inside the extract, which preserves caching because it is data-driven, not time-driven.For example:{ FIXED : MAX([Date]) }Then filtering where [Date] = { FIXED : MAX([Date]) } ensures only the most recent date in the dataset is shown.

Tableau’s documentation on performance emphasizes that caching is maximized when calculations depend only on the data itself and not on functions like TODAY() or relative filters.

A FIXED LOD provides the best caching performance and correctly returns the most recent date based on the dataset rather than the current system date.

Tableau extract caching behavior describing how data-dependent filters cache better than time-dependent filters.

LOD Expressions guidance recommending FIXED for identifying values like “latest date in the dataset.”

Tableau performance guidelines discouraging TODAY() and relative date filters when caching is important.

The most user-friendly solution is to use Dynamic Zone Visibility in combination with a Show/Hide Button. This approach allows the dashboard to dynamically display only the relevant quick filters based on the viewer’s selection of shops or warehouses, thus reducing clutter and focusing the user’s attention on applicable filters. The Show/Hide Button further enhances the user experience by allowing viewers to toggle the visibility of the filter container, providing a clean and organized dashboard interface1.

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.