Tableau TCC-C01 - Tableau Certified Consultant

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.

To identify which workbooks need to be updated to point to a new data source after a migration, a consultant should use Tableau Advanced Management. This component of Tableau provides comprehensive management capabilities including the ability to track workbook dependencies and data source usage across your entire Tableau environment. Using Tableau Advanced Management allows consultants to assess the impact of changes in the data source on connected workbooks and efficiently manage updates.

To effectively curate and document data for obtaining data lineage, particularly from a data source connected to a data lake, the recommended tool is:

Tableau Catalog with Tableau Data Management Add-on: This add-on enhances the capabilities of Tableau Catalog, providing extensive features for data management, including detailed data lineage, impact analysis, and metadata management.

Functionality: The Tableau Catalog with the Data Management Add-on allows users to see the full history and lineage of the data, trace its usage across all Tableau content, and understand dependencies. It also facilitates better governance and transparency in data handling.

Why Choose this Tool: For a client needing comprehensive data lineage and documentation capabilities, this add-on ensures that data stewards and users can maintain and utilize a well-managed data environment. It supports robust data governance practices necessary for large and complex data ecosystems like those typically associated with data lakes.

ReferencesThe recommendation is based on the functionalities offered by the Tableau Data Management Add-on, as described in Tableau's official documentation on managing and documenting data sources for enhanced governance and operational efficiency.

To transform theOrderldfield in Tableau Prep, the Split option is the most efficient and straightforward method. Here’s how you can apply it:

In Tableau Prep, drag your dataset into the flow.

Click on theOrderldfield in the workspace to select it.

Look for the option in the toolbar that says "Split" and select it.

Choose "Automatic Split" if the delimiters (such as hyphens) are consistent; Tableau Prep should automatically detect the hyphen as the delimiter and split theOrderldinto multiple new fields.

The dataset should now show new columns: one for the country code (CA, FR, US), one for the year (2017), and one for the order number (152156, 152157, etc.).

The Split option works effectively here because it automatically identifies and uses the hyphen as the delimiter to divide the originalOrderldinto the desired components without manual specification of conditions or writing any formulas.

ReferencesThis procedure is based on the standard functionalities provided in Tableau Prep for splitting a field into multiple columns based on a delimiter, as described in the Tableau Prep user guide.

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 left part of the formula{ FIXED [Order ID] : SUM([Sales]) }calculates the total sales for each individual order.

The right part{ FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) }calculates the average sales per order within each region.

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.

The discrepancy between the count of workbooks in the Connected Workbooks tab and the lineage count in Tableau Catalog can occur because of user permissions. In Tableau Data Management, the visibility of connected workbooks is subject to the permissions set by administrators. If a user does not have permission to view certain workbooks, they will not see them listed in the Connected Workbooks tab, even though these workbooks are part of the data source’s lineage and are counted in Tableau Catalog.

References:This explanation is based on the functionality of Tableau Data Management and Tableau Catalog, which includes managing user permissions and access to workbooks.The information is supported by Tableau’s official documentation on data management and security practices1.

The correct calculation to determine the average number of days a customer must wait before a product is shipped is to first find the shipping date, which is the first day of the following month after the order date. This is done usingDATETRUNC('month', DATEADD('month', 1, [Order Date])). Then, the average difference in days between the order date and the shipping date is calculated usingAVG(DATEDIFF('day', [Order Date], [Calc1])). This approach ensures that the average wait time is calculated in days, which is the most precise measure for this scenario.

References:The solution is based on Tableau’s date functions and their use in calculating differences between dates, which are well-documented in Tableau’s official learning resources and consultant documents12.

To calculate the average waiting days from order placement to shipping, where shipping occurs on the first day of the following month:

Calculate Shipping Date (Calc1): Use the DATEADD function to add one month to the order date, then apply DATETRUNC to truncate this date to the first day of that month. This represents the shipping date for each order.

Calculate Average Wait Time (Calc2): Use DATEDIFF to calculate the difference in days between the original order date and the calculated shipping date (Calc1). Then, use AVG to average these differences across all orders, giving the average number of days customers wait before their products are shipped.

References:

Date Functions in Tableau: Functions like DATEADD, DATETRUNC, and DATEDIFF are used to manipulate and calculate differences between dates, crucial for creating metrics that depend on time intervals, such as customer wait times in this scenario.

The Tableau Catalog is part of the Tableau Data Management Add-on and is designed to help users understand the data they are using within Tableau. It provides a comprehensive view of all the data assets in Tableau Server or Tableau Online, including databases, tables, and fields. It can help identify issues such as data quality, data lineage, and impact analysis. In this case, where different departments are getting different answers to the same business questions, the Tableau Catalog can be used to track down inconsistencies and ensure that everyone is working from the same, reliable data source.

References:The recommendation for using Tableau Catalog is based on its features that support data discovery, quality, and governance, which are essential for resolving data inconsistencies across different departments12.

When different departments report different answers to the same business questions using the same databases and tables, the issue often lies in how data is being accessed and interpreted differently across departments. Tableau Catalog, a part of Tableau Data Management, can be used to solve this problem:

Visibility: Tableau Catalog gives visibility into the data used in Tableau, showing users where data comes from, where it’s used, and who’s using it.

Consistency and Trust: It helps ensure consistency and trust in data by providing detailed metadata management that can highlight discrepancies in data usage or interpretation.

Usage Metrics and Lineage: It offers tools for tracking usage metrics and understanding data lineage, which can help in identifying why different departments might see different results from the same underlying data.

References:

Tableau Catalog Usage: The Catalog is instrumental in providing a detailed view of the data environment, allowing organizations to audit, track, and understand data discrepancies across different users and departments.

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.