Tableau TCC-C01 Practice Test - Questions Answers

To build efficient extracts from large data sources, it is crucial to minimize the load and optimize the performance of the extracts:

A . Keep only the data required for analysis by using extract filters: This best practice involves using filters to reduce the volume of data extracted, thus focusing only on the data necessary for analysis.

B . Use aggregate data for visible dimensions, whenever possible: Aggregating data at the time of extraction reduces the granularity of the data, which can significantly improve performance and reduce the size of the extract.

E . Hide all unused fields: Removing fields that are not needed for analysis from the extract reduces the complexity and size of the data model, which in turn enhances performance and speeds up load times.

These practices are endorsed in Tableau's official documentation and training sessions as effective ways to enhance the performance of Tableau extracts and optimize dashboard responsiveness.

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 of TODAY()-1 ensures 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.

Reference This 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.

When a filter is applied directly to the view, it can affect the calculation of percentages in a histogram because it changes the underlying data that the quick table calculation is based on. To avoid this, adding the [Profit] filter to the context will maintain the original calculation of percent shares while filtering out bins with less than a 15% share. This is because context filters are applied before any other calculations, so the percent shares calculated will be based on the context-filtered data, thus preserving the integrity of the original percent labels.

When a histogram is created showing the distribution of profits with labels indicating percent shares using a quick table calculation, and a need arises to limit the view to bins with at least a 15% share, applying a standard profit filter directly may undesirably alter how the percent labels calculate because they depend on the overall distribution of data. Placing the [Profit] filter into the context makes it a 'context filter,' which effectively changes how data is filtered in calculations:

Create a Context Filter: Right-click on the profit filter and select 'Add to Context'. This action changes the order of operations in filtering, meaning the context filter is applied first.

Adjust the Percent Calculation: With the profit filter set in the context, it first reduces the data set to only those profits that meet the filter criteria. Subsequently, any table calculations (like the percent share labels) are computed based on this reduced data set.

View Update: The view now updates to display only those bins where the profits are at least 15%, and the percent share labels recalculated to reflect the distribution of only the filtered (contextual) data.

Context Filters in Tableau: Context filters are used to filter the data passed down to other filters, calculations, the marks card, and the view. By setting the profit filter as a context filter, it ensures that calculations such as the percentage shares are based only on the filtered subset of the data.

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.

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.

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.

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.

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.

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

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.

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 correctly calculate the percent of total sales for a particular region compared to all regions, and to ensure that the calculation does not get inadvertently recalculated with each region filter application, the recommended calculation is:

{FIXED [Region]: sum([Sales])}: This part of the formula computes the sum of sales for each region, regardless of any filters applied to the view. It uses a Level of Detail expression to fix the sum of sales to each region, ensuring that filtering by regions won't affect the calculated value.

SUM([Sales]): This part computes the total sum of sales across all regions and is recalculated dynamically based on the filters applied to other parts of the dashboard or worksheet.

Combining the two parts: By dividing the fixed regional sales by the total sales, we get the proportion of sales for each region as compared to the total. This calculation ensures that while the denominator adjusts according to filters, the numerator remains fixed for each region, accurately reflecting the sales percentage without being affected by the region filter directly.

Reference This calculation follows Tableau's best practices for using Level of Detail expressions to manage computation granularity in the presence of dashboard filters, as outlined in the Tableau User Guide and official Tableau training materials.

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.

Reference This 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.

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.

Reference This 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.