Free Practice Questions for Tableau TCC-C01 Exam

To improve the loading time of the pipeline dashboard, which primarily suffers from long query execution times due to a comprehensive Relative Date filter:

Relative Date Filter Issue: The existing Relative Date filter on "Created Date" covers a broad range (5 years), leading to significant data processing overhead as it includes granular date calculations over a large dataset.

Optimized Approach: By replacing the Relative Date filter with a Multiple Values (Dropdown) filter based on YEAR([Created Date]), the filter granularity is reduced. Filtering by year simplifies the query by limiting the volume of data processed and reducing the complexity of the filter condition.

Implementation Benefit: This approach still provides the flexibility to view data across different years but does so by reducing the load on the database during query execution, which is critical for improving the performance of the dashboard.

ReferencesThis recommendation aligns with Tableau performance optimization strategies, specifically regarding the management of date filters to minimize their impact on query load, as discussed in Tableau performance tuning sessions and guides.

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.

For a consultant designing a dashboard to be consumed across multiple device types, the best approach is:

Multi-device Layout: Tableau provides the capability to design device-specific layouts within a single dashboard. This feature allows the dashboard to adapt its layout to best fit the screen size and orientation of desktops, tablets, and phones.

Fixed Size Layouts: By fixing the size of each layout, the consultant can ensure that the dashboard appears consistent and maintains the intended design elements and user experience across devices. Fixed sizes prevent components from resizing in ways that could disrupt the dashboard’s readability or functionality.

Implementation: In Tableau, you can create these layouts by selecting 'Device Preview' and adding custom layouts for each device type. Here, you define the dimensions and the positioning of sheets and controls tailored to each device's typical viewing mode.

ReferencesThis approach leverages Tableau’s device designer capabilities, which are specifically designed to optimize dashboards for multiple viewing environments, ensuring a seamless user experience regardless of the device used. This functionality is well documented in Tableau’s official guides on creating and managing device-specific dashboards.

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.

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.

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.

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 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.

References:The information about the Explorer (can publish) role and its capabilities can be found in the official Tableau documentation on site roles and permissions12.This role is appropriate for users who need to interact with published content and create new visualizations without the need for full site administration or advanced content creation tools that come with the Creator role3.

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.