Tableau TCC-C01 Tableau Certified 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:
• Create a Calculation for Year-over-Year Percentage Change:

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

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• Replace the Original Measure with the Year-over-Year Percentage Change in the Crosstab:
• Save Your Changes:

References:

• Tableau Help: Offers guidance on creating calculated fields and using table calculations.
• Tableau Desktop User Guide: Provides instructions on formatting and saving worksheets.

These steps allow you to manipulate data within Tableau effectively, using table calculations to analyze trends and changes in sales data by category and segment over years.

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:
• Calculate Cumulative Sales Percentage:

(RUNNING_SUM(SUM([Sales])) / TOTAL(SUM([Sales])) [Sales]))

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• Add the Cumulative Sales Percentage Line to the Chart:
• Format the Cumulative Sales Percentage Line:
• Save Your Changes:

References:

• Tableau Help: Provides additional details on creating calculated fields and customizing line charts.
• Tableau User Guide: Offers extensive instructions on formatting charts, including line types and axis synchronization.

By following these steps, you will successfully add a cumulative sales percentage line to your chart, enhancing the visualization to reflect the incremental contribution of each product to the overall sales in a dynamic and informative manner.

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:
• Format and Shape the Data:
• Calculate Total Number of Records:
• Enter the Value into the Total Records Parameter:
• Save Your Changes:

References:

• Tableau Desktop Guide: Provides detailed instructions on managing data types, creating calculated fields, and updating parameters.
• Tableau Data Shaping Techniques: Outlines effective methods for manipulating and structuring data for analysis.

This process will ensure the data in the "US Population Estimates" is accurately shaped according to the specified format and that the total number of records is correctly calculated and entered into the designated parameter. This thorough approach ensures data integrity and accuracy in reporting.

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:
• Modify the Percent Contribution Calculated Field:

{FIXED [Manufacturer]: SUM([Quantity])} / {SUM([Quantity])}Quantity])}

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• Enter Percentage for Newell:
• Save Your Changes:

References:

• Tableau Help: Offers detailed guidance on using LOD expressions for precise and context-independent aggregations.
• Tableau Desktop User Guide: Provides comprehensive instructions on managing calculated fields and parameters, ensuring accurate data analysis.

By following these steps, you will have successfully updated the calculation for percent contribution using LOD expressions, providing a more accurate analysis of each manufacturer's contribution to the total quantity. Moreover, updating the parameter with Newell's specific contribution rounds out the task by reflecting precise data inputs for reporting or further analysis.

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:
• Construct the Pareto Chart:
• Add Reference Lines for the Pareto Principle:
• Adjust the Appearance of the Chart:
• Save Your Changes:

References:

• Tableau Help: Offers detailed guidance on creating Pareto charts and adding reference lines.
• Tableau Visualization Best Practices: Provides tips on effectively displaying cumulative data and principles such as Pareto.

By following these steps, you will have successfully enhanced the City Pareto worksheet to include a complete Pareto chart with reference lines that illustrate how the sales data compares to the Pareto principle, making it easier to analyze and communicate the distribution of sales across cities.

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:
• Start Performance Recording:
• Open the Property Transactions Dashboard and Reset Filters:
• Stop the Performance Recording:
• Save the Performance Recording:
• Create a New Worksheet for Performance Analysis:
• Customize and Finalize the Bar Chart:
• Save Your Work:

References:

• Tableau Help Documentation: Provides guidance on how to start and stop performance recordings and analyze them.
• Tableau Visualization Techniques: Offers tips on creating effective bar charts for performance data.

By following these steps, you have successfully recorded and analyzed the performance of the Property Transactions dashboard, providing valuable insights into how each component of the dashboard contributes to the overall load time. This analysis is crucial for optimizing dashboard performance and ensuring efficient data visualization.

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.

Using an extract filter is an effective way to reduce the amount of data in a Tableau extract. Extract filters allow you to specify a subset of the data to include, which can significantly decrease the size of the extract by excluding unnecessary data. This is particularly useful when you only need partial data from a larger SQL Server data source.

References:The recommendation to use extract filters to reduce data size is supported by Tableau’s best practices for optimizing extracts.These practices suggest keeping the extract’s data set short through filtering1.Additionally, discussions in the Tableau Community confirm that hiding fields and using extract filters before extracting data can help reduce the extract size2.

When dealing with large datasets in SQL Server and needing to create a manageable extract in Tableau, using an extract filter is the most direct and effective method to limit the data included:

• Extract Filter: This involves setting filters that apply directly when the data is extracted from the source. This means that only the data meeting the specified criteria will be extracted and loaded into Tableau, significantly reducing the size of the extract.
• To apply an extract filter, in the Data Source page in Tableau, drag the fields you want to filter by to the Filters shelf. Then, configure the desired filter criteria. When you create the extract, choose the option to "Add Filters to Extract" and select the configured filters. This ensures that only the data that meets these conditions is extracted from the SQL Server.

This approach not only minimizes the data volume but also speeds up performance in Tableau because it processes a smaller subset of the full dataset.

ReferencesThis procedure is described in detail in Tableau's help documentation on managing extracts and optimizing performance by using extract filters, which is recommended for scenarios involving large datasets or when specific subsets of data are required for analysis.

To flag orders with sales higher than the regional average, the correct calculated field would compare the sum of sales for each order against the average sales of all orders within the same region:

• Correct Formula:{ FIXED [Order ID] : SUM([Sales]) } > { FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) }
• This calculation uses a Level of Detail (LOD) expression:
• The>operator is used to compare these two values to determine if the sales for each order exceed the regional average.

ReferencesThis formula utilizes Tableau's LOD expressions to perform complex comparisons across different dimensions of the data, as explained in Tableau's official training materials on LOD calculations.

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.

ReferencesThe 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.

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 ofTODAY()-1ensures 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.

ReferencesThis 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.

To calculate the Total Sales Value for all categories as displayed in the table, an LOD expression is ideal. An LOD calculation in Tableau allows you to compute values at the data level that is different from the view level. In this case, since the Total Sales Value appears consistent across different sub-categories within each category, an LOD expression can be used to fix the Total Sales Value irrespective of the sub-category detail. Here's how to set it up:

• Go to the Calculations area by right-clicking in the data pane and selecting "Create Calculated Field".
• Enter a name for the calculation, such as "Total Sales Value".
• Enter the LOD expression:{ FIXED [Category] : SUM([Sales]) }. This calculation fixes the total sales to the category level, effectively summing sales for all sub-categories within each category, irrespective of how the data is broken down in the view.
• Drag this new calculated field into your visualization alongside the existing measures.

This method ensures that the Total Sales Value reflects the total for each category across all its sub-categories, matching the uniform values shown across different rows for each category in your table.

ReferencesThe explanation utilizes the concept of Level of Detail calculations in Tableau, which allows for advanced aggregations independent of the view level details. This concept is covered extensively in Tableau's official documentation and relevant training materials such as Tableau's online help resources.

Given the need for up-to-date data on a daily basis and weekly revisions, the best approach is to use an incremental extract refresh daily to update the data source with new transactions. On Saturdays, when incorrect transactions are revised, a full extract refresh should be performed to incorporate all revisions and ensure the data’s accuracy.This strategy allows end users to have access to the most current data throughout the week while also accounting for any necessary corrections12.

References:The solution is based on best practices for managing data sources in Tableau Server, which recommend using incremental refreshes for frequent updates and full refreshes when significant changes or corrections are made to the data12.

The most efficient way to limit report visibility to specific user groups on Tableau Server is to manage permissions directly on the server. By granting access to the report only to the sales and marketing user groups, the client ensures that only members of these groups can view the report. Thismethod is straightforward and does not require the additional steps involved in setting up row-level security or user filters.

References:The approach is supported by best practices in managing user permissions and visibility on Tableau Server, as described in the Tableau Community and official Tableau resources12.

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.

References:The best practices for optimizing workbook performance in Tableau are well-documented in Tableau’s official resources, including the Tableau Help Guide and the Designing Efficient Workbooks whitepaper, which provide detailed recommendations on how to streamline workbooks for better performance12.

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.

References:The use of personal access tokens (PATs) in Tableau and the procedure for renewing them are documented in Tableau’s official resources.It is noted that PATs are long-lived authentication tokens that can be revoked and renewed to manage access securely1.Additionally, there have been discussions in the Tableau Community regarding issues with concurrent PAT access, which further supports the need to manage tokens effectively2.