Analytics-Con-301 Salesforce Certified Tableau Consultant Free Practice Exam Questions (2025 Updated)

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 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 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 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.

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%.

The Explorer (can publish) site role in Tableau is designed for users who need to access, edit, and create visualizations based on published data sources, even when they are outside the company network. This role allows users to perform web editing and save their work, making it suitable for sales users who need these capabilities. It is also a cost-effective option as it does not require the full capabilities and associated costs of the Creator license.

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.

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 workbook, it is best practice to streamline the data being used. This can be achieved by using filters to limit the data to only what is necessary for analysis, hiding fields that are not being used to reduce the complexity of the data model, and aggregating values to simplify the data and reduce the number of rows that need to be processed. These steps can help reduce the load on the server and improve the speed of the workbook.

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 considering a migration from Tableau Server to Tableau Cloud, two critical factors to consider are the client’s need for single sign-on (SSO) and the volume of concurrent extract refreshes.

Single Sign-On (SSO): Tableau Cloud supports SSO, which can streamline user authentication and enhance security. If the client plans to leverage SSO, Tableau Cloud may be a suitable choice1.

Concurrent Extract Refreshes: The number of concurrent extract refreshes is a significant factor because it impacts performance and resource allocation. Tableau Server might be more appropriate if the client has a high volume of concurrent extract refreshes, as it allows for more control over the infrastructure to manage these workloads2.

The approach of using user filters to control access to data on profitability for Groups 2 and 3, combined with a calculated field that restricts the visibility of profit value to only Group 2, aligns with Tableau’s best practices for managing content permissions. This method ensures that each group sees only the data they are permitted to view, with Group 1 not seeing any profitability information, Group 2 seeing both profit and profit margin, and Group 3 seeing only the profit margin without the actual profit values. This setup can be achieved through Tableau Server’s permission capabilities, which allow for detailed control over what each user or group can see and interact with12.

To minimize the impact on users when reverting to a previous version of a published data source in Tableau Server, the consultant should use the built-in revision history feature. By selecting a previous version from the revision history and clicking ‘Restore’, the data source will revert to that version without the need for a full server backup restoration or manual recreation of the data source. This process is quick and has the least amount of disruption to users.

To improve performance by moving calculations to the data layer and materializing them in the extract, the consultant should choose calculations that benefit from pre-computation and significantly reduce the load during query time:

Aggregation-Level Calculation: The formula SUM([Profit])/SUM([Sales]) calculates a ratio at an aggregate level, which is ideal for pre-computation. Materializing this calculation in the extract means that the complex division operation is done once and stored, rather than being recalculated every time the report is accessed.

Performance Improvement: By pre-computing this aggregate ratio, Tableau can utilize the pre-calculated fields directly in visualizations, which speeds up report loading and interaction times as the heavy lifting of data processing is done during the data preparation stage.

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 allows certain calculations to be materialized in extracts, meaning they are precomputed and stored inside the .hyper file to improve performance.

According to Tableau’s extract documentation:

Materializable calculations must be compatible with the extract engine and must not depend on dynamic, view-based, or post-query logic.

Only row-level calculations and aggregation-level calculations without dependencies on runtime context can be materialized.

Tableau cannot materialize any calculation containing:

Table calculation functions

Functions requiring post-aggregation logic

View-dependent elements

Parameters that need runtime evaluation

Evaluation of the choices:

A. A row-level calculation — Correct

Row-level calculations operate on each record individually before aggregation.

Tableau documentation specifies that these calculations can be pushed down into the extract and materialized because they do not depend on the visualization or user interaction.

Examples include concatenation, arithmetic, string manipulation, and row-based logic such as:

[Sales] * [Quantity] or IF [Region] = 'West' THEN 1 END

These can be precomputed inside the extract, improving performance.

B. A calculation that contains table calculation functions — Not allowed

Table calculations (WINDOW_SUM, INDEX, RUNNING_SUM, RANK, etc.) depend on the table structure after aggregation and query execution.

Therefore, Tableau documentation states they cannot be materialized in extracts.

C. A calculation that contains parameters — Not allowed

Parameters are evaluated at runtime, meaning the user can change their value.

Because of this, Tableau cannot permanently compute and store such a calculation inside an extract.

D. A calculation that contains an aggregation — Generally not materialized

Aggregated calculations often depend on query context and cannot always be materialized.

Only simple, context-free aggregations might be materialized, but Tableau explicitly warns that aggregations are not guaranteed candidates for extract materialization.

Thus, this is not the best answer compared to row-level logic.

Conclusion

Only row-level calculations meet Tableau’s exact requirements for materialization in extracts.

References From Tableau Consultant Documentation

Tableau Extract documentation describing materializable calculation types.

Tableau guidance stating table calculations and parameter-dependent calculations cannot be materialized.

Extract optimization guidelines describing row-level logic as eligible for materialization.

For modeling shipping costs based on varying vendor contracts and ensuring speed in dashboard performance, the suggested approach involves:

Calculated Field with Parameter: Utilize a calculated field that dynamically references a user-selected parameter for the shipping vendor. This parameter adjusts the cost calculations based on selected vendor characteristics (like volume and speed).

Aggregate Results: After calculating individual shipping costs, aggregate these costs to provide a concise, summarized view of potential expenses for the upcoming week. This method ensures the dashboard remains performant by reducing the load of processing individual line items in real-time.

Why This Works: By using parameters and calculated fields, the dashboard can quickly adapt to user inputs without needing to re-query the entire dataset. Aggregating the results further improves performance and user experience by simplifying the output.

References

This strategy leverages Tableau's capability to handle dynamic calculations with parameters and is recommended for scenarios where performance and user-driven interaction are priorities. Tableau’s performance optimization resources and dashboard design guidelines detail these techniques.

Comprehensive and Detailed Explanation From Exact Extract:

Key Tableau Catalog behaviors:

Connected workbooks = any workbook linked to the published data source.

Impacted workbooks = only workbooks that use the specific field.

If a workbook connects to the data source but never uses the field, it appears as “connected” but not impacted.

This explains EXACTLY why:

7 workbooks are connected

Only 6 use the changed field

Therefore only 6 are impacted

This matches Option C.

Why the other options are incorrect:

A. Field used twice

Still counts as one workbook — does not explain discrepancy.

B. Permission issue

If permissions blocked visibility, the data source would not list 7 connections.

D. Custom SQL use

Catalog can still detect field usage through metadata lineage; Custom SQL does NOT hide workbook dependency.

Thus, only Option C logically explains the scenario.

Data Catalog lineage rules: “Connected vs. Impacted” distinction.

Field-level impact analysis documentation.

Workbook dependency logic within Tableau Catalog.