TCC-C01 Tableau Certified Consultant Questions and Answers

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.

References:

• Tableau Help and Support: Offers comprehensive tutorials and guidelines on using different tools for migrating various types of content, including the specific requirements for migrating Tableau Prep flows which are not covered by the standard content migration tool.

For implementing row-level security (RLS) without the Tableau Data Management Add-on, the best approach is to integrate user filters into the published data source:

• Creating User Filters on Published Data Source: This method involves defining user filters that apply directly to the data source before it is published to the Tableau Server. This ensures that any workbook or view leveraging this data source inherently respects the row-level security settings.
• To implement this, create a calculated field in Tableau that defines the security logic, typically using a formula that references user functions (likeUSERNAME()orISMEMBEROF()). Drag this field to the Filters shelf and configure it to match the security rules (who can see what data).
• Once configured, publish the data source to Tableau Server with these filters in place. This approach centralizes security management, making it easier to maintain and update security policies as they are applied universally to all workbooks using this data source.

This strategy is safe as it reduces the risk of accidental data exposure through individual workbook misconfiguration and simplifies long-term maintenance of security policies.

ReferencesThis method follows Tableau’s best practices for implementing row-level security as detailed in Tableau’s security management resources. It ensures robust, maintainable security measures that scale with organizational needs without requiring additional add-ons.

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.

References:The performance hierarchy of data types in Tableau calculations is documented in resources that discuss best practices for optimizing Tableau performance1.

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.

References:The decision between Tableau Server and Tableau Cloud should be based on specific organizational needs, including security, compliance, performance, and scalability.The official Tableau resources provide guidance on these factors12.Additionally, discussions in the Tableau Community highlight the importance of considering these elements when choosing between Tableau Server and Tableau Cloud1.

Moving calculations to the data layer and materializing them in the extract can significantly improve the performance of reports in Tableau. The calculationZN([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.

References:The answer is based on the best practices for creating efficient calculations in Tableau, as described in Tableau’s official documentation, which suggests using basic and aggregate calculations to improve performance1.Additionally, the process of materializing calculations in extracts is detailed in Tableau’s resources2.

Given that all files share the same schema and have a common element in their file names, the wildcard union is an optimal approach to combine these files before performing any transformations. This strategy offers the following advantages:

• Efficient Data Combination: Wildcard union allows multiple files with a common naming scheme to be combined into a single dataset in Tableau, streamlining the data preparation process.
• Uniform Schema Handling: Since all files share the same schema, wildcard union ensures that the combined dataset maintains consistency in data structure, making further data manipulation more straightforward.
• Pre-Transformation Combination: Combining the files before applying transformations is generally more efficient as it reduces redundancy in transformation logic across multiple files. This means transformations are written and processed once on the unified dataset, rather than repeatedly for each individual file.

References:

• Wildcard Union in Tableau: This feature simplifies the process of combining multiple similar files into a single Tableau data source, ensuring a seamless and efficient approach to data integration and preparation.

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.

References:

• Handling NULL Values: Replacing NULL values with a valid unknown option ensures that all data is included in the analysis, and integrity between tables is maintained, thereby optimizing the performance and accuracy of the dashboard.

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 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.

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 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 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 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.