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質問 # 18
A client has a dashboard that uses a bar chart to visualize sales by Sub-Category and a detail table that has all the orders for the products within Sub- Category. The table has more than 10,000 rows of data and is slow to load.
A consultant plans to add an action so when the client interacts with the bar chart, only the relevant data appears in the table.
What will provide the fastest rendering of the dashboard?
- A. Add a highlight action and set Target Highlighting to Sub-Category.
- B. Add a highlight action and set Target Highlighting to All Fields.
- C. Add a filter action, set "Run action on" to Select, and set "Clearing the selection will" to Exclude all values.
- D. Add a filter action, set "Run action on" to Menu, and set "Clearing the selection will" to Show all values.
正解：C
解説：
To optimize the dashboard rendering, particularly when dealing with a large dataset, a filter action is the most effective tool. Here's why the specified choice is optimal:
* Add a filter action: This action creates a direct filter on the detail table based on the selection in the bar chart. It ensures that only data related to the selected sub-category is loaded into the table, significantly reducing load time and improving performance.
* Set "Run action on" to Select: This setting means the filter action will be triggered as soon as the user selects a bar in the bar chart. Immediate activation of the filter ensures that the dashboard is interactive and responsive.
* Set "Clearing the selection will" to Exclude all values: When the selection is cleared, this setting ensures that no data is shown, which avoids loading the entire dataset unnecessarily. This maintains performance when no sub-category is actively selected.
ReferencesThis strategy follows Tableau's performance best practices by using actions to limit the amount of data processed and rendered, as detailed in the Tableau User Guide and training materials on Dashboard Actions for optimizing large datasets.
質問 # 19
A client has a published dashboard. They change the dashboard and then republish it. Now, usersreport that their web browser bookmarks to the dashboard are broken.
What are two possible causes for this issue? Choose two.
- A. The dashboard was published with a new name.
- B. Tableau Server was upgraded.
- C. The dashboard was published to a different project.
- D. New credentials were embedded into the data source.
正解：A、C
解説：
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.
質問 # 20
A client wants to flag orders that have sales higher than the regional average.
Which calculated field will produce the required result?
- A. [Sales]
>
{ FIXED [Order ID] : SUM([Sales]) } - B. { FIXED [Order ID] : SUM([Sales]) }
>
{ FIXED [Region] : SUM([Sales]) } - C. { FIXED [Order ID] : SUM([Sales]) }
>
{ FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) } - D. { FIXED [Order ID] : SUM([Sales]) }
>
{ INCLUDE [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) }
正解：C
解説：
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.
質問 # 21
A consultant creates a histogram that presents the distribution of profits across a client's customers. The labels on the bars show percent shares. The consultant used a quick table calculation to create the labels.
Now, the client wants to limit the view to the bins that have at least a 15% share. The consultant creates a profit filter but it changes the percent labels.
Which approach should the consultant use to produce the desired result?
- A. Filter with a table calculation WINDOW_AVG(MIN([Profit]), first(), last())
- B. Use a calculation with TOTAL() function instead of a quick table calculation.
- C. Add the [Profit] filter to the context.
- D. Filter with the table calculation used to create labels.
正解：C
解説：
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.
References:The solution is based on the principles of context filters and their order of operations in Tableau, which are documented in Tableau's official resources and community discussions123.
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.
References:
* 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.
質問 # 22
A stakeholder has multiple files saved (CSV/Tables) in a single location. A few files from the location are required for analysis. Data transformation (calculations) is required for the files before designing the visuals. The files have the following attributes:
. All files have the same schema.
. Multiple files have something in common among their file names.
. Each file has a unique key column.
Which data transformation strategy should the consultant use to deliver the best optimized result?
- A. Apply the data transformation (calculations) in each require file and do the wildcard union to combine/merge before designing the visuals.
- B. Apply the data transformation (calculations) in each require file and do the join to combine/merge before designing the visuals.
- C. Use wildcard Union option to combine/merge all the files together before doing the data transformation (calculations).
- D. Use join option to combine/merge all the files together before doing the data transformation (calculations).
正解：C
解説：
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.
質問 # 23
For a new report, a consultant needs to build a data model with three different tables, including two that contain hierarchies of locations and products. The third table contains detailed warehousing data from all locations across six countries. The consultant uses Tableau Cloud and the size of the third table excludes using an extract.
What is the most performant approach to model the data for a live connection?
- A. Blending the first two tables with the third
- B. Joining the tables in Tableau Desktop
- C. Relating the tables in Tableau Desktop
- D. Joining the tables in Tableau Prep
正解：C
解説：
For a performant live connection in Tableau Cloud, especially when dealing with large datasets that preclude the use of extracts, relating the tables in Tableau Desktop is the recommended approach. This method allows for flexibility in how the data is queried and can improve performance by leveraging Tableau's relationships feature, which optimizes queries for the underlying database.
References:The best practices for live connections in Tableau Cloud suggest using relationships to manage complex data models efficiently1.Additionally, Tableau's documentation on connecting data sources recommends using relationships for better performance with live connections2.
質問 # 24
A company has a data source for sales transactions. The data source has the following characteristics:
. Millions of transactions occur weekly.
. The transactions are added nightly.
. Incorrect transactions are revised every week on Saturday.
The end users need to see up-to-date data daily.
A consultant needs to publish a data source in Tableau Server to ensure that all the transactions in the data source are available.
What should the consultant do to create and publish the data?
- A. Publish an incremental extract refresh every day and publish a secondary data set containing data revisions.
- B. Publish an incremental extract refresh every day and perform a full extract refresh every Saturday.
- C. Publish an incremental refresh every Saturday.
- D. Publish a live connection to Tableau Server.
正解：B
解説：
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.
質問 # 25
From the desktop, open the CC workbook.
Open the Manufacturers worksheet.
The Manufacturers worksheet is used to
analyze the quantity of items contributed by
each manufacturer.
You need to modify the Percent
Contribution calculated field to use a Level
of Detail (LOD) expression that calculates
the percentage contribution of each
manufacturer to the total quantity.
Enter the percentage for Newell to the
nearest hundredth of a percent into the
Newell % Contribution parameter.
From the File menu in Tableau Desktop, click
Save.
正解：
解説：
See the complete Steps below in Explanation:
Explanation:
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:
* Double-click on the CC workbook from the desktop to open it in Tableau Desktop.
* Navigate to the Manufacturers worksheet by selecting its tab at the bottom of the window.
* Modify the Percent Contribution Calculated Field:
* Navigate to the Data pane and find the "Percent Contribution" calculated field.
* Right-click on the "Percent Contribution" field and select 'Edit'.
* Modify the formula to incorporate an LOD expression that calculates the total quantity across all manufacturers and the specific quantity per manufacturer:
{FIXED [Manufacturer]: SUM([Quantity])} / {SUM([Quantity])}Quantity])}
* This formula uses{FIXED [Manufacturer]: SUM([Quantity])}to compute the total quantity contributed by each manufacturer, regardless of other dimensions in the view. The total quantity
{SUM([Quantity])}calculates the grand total across all manufacturers. The division calculates the percentage contribution.
* Click 'OK' to save the updated calculated field.
* Enter Percentage for Newell:
* With the updated "Percent Contribution" field, drag it onto the view to update the chart or table.
* Identify the value corresponding to 'Newell' in the updated visualization.
* Round this value to the nearest hundredth of a percent as required.
* Enter this value into the "Newell % Contribution" parameter. To do this, locate the parameter in the Data pane or on the dashboard, right-click it, and choose 'Edit'. Enter the calculated percentage for Newell.
* Save Your Changes:
* From the File menu, click 'Save' to store all the modifications you have made to the workbook.
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.
質問 # 26
A client is using the Tableau Content Migration Tool to move content from an old Tableau Server to a new Tableau Server.
Which content will need to be moved using a different tool or process?
- A. Published data sources that use extracts
- B. Workbooks
- C. Published data sources that use live connections
- D. Tableau Prep flows
正解：D
解説：
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.
質問 # 27
A client's dashboard has two sections dedicated to their shops and warehouses shown when a viewer chooses either shops or warehouses with a parameter.
There are a few quick filters that apply to both, while others apply to only shops or only warehouses.
Currently, the quick filters are all shown at the left side of the dashboard. The client wants to hide all filters, but when shown, make it easy for the viewer to find the quick filters that work for only shops or only warehouses.
Which solution should the consultant recommend that meets the client's needs and is most user-friendly?
- A. Use Dynamic Zone Visibility to show only the quick filters that apply with the chosen parameter value and a Show/Hide Button to hide container with all the filters.
- B. Use Dynamic Zone Visibility to inform viewers which quick filters apply to warehouses or shops.
- C. Divide the quick filters into three groups: General, for shops. Place the general filters on the left of dashboard for warehouses. Place other filters next to the sections to which they apply.
- D. Hide container with all quick filters with a Show/Hide Button.
正解：A
解説：
The most user-friendly solution is to use Dynamic Zone Visibility in combination with a Show/Hide Button.
This approach allows the dashboard to dynamically display only the relevant quick filters based on the viewer's selection of shops or warehouses, thus reducing clutter and focusing the user's attention on applicable filters.The Show/Hide Button further enhances the userexperience by allowing viewers to toggle the visibility of the filter container, providing a clean and organized dashboard interface1.
References:Dynamic Zone Visibility is a feature in Tableau that enables dashboard elements to appear or disappear based on the value of a field or parameter1.This functionality is ideal for creating interactive and user-friendly dashboards that adapt to user interactions and selections1.
質問 # 28
A client needs to design row-level security (RLS) measures for their reports. The client does not currently have Tableau Data Management Add-on, and it may be an option in the future.
What should the consultant recommend as the safest and easiest way to manage for the long term?
- A. Create User filters for each report using a table joined to its data source and using the option Apply to All Sheet Using the Data Source.
- B. Create User filters based on data policies and apply them to a published data source.
- C. Create User filters in each view of each report using set filters and option Server/Create User Filter.
- D. Create User filters based on data policies and apply them to views using set filters and option Server/Create User Filter.
正解：B
解説：
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.
質問 # 29
A client collects information about a web browser customers use to access their website. They then visualize the breakdown of web traffic by browser version.
The data is stored in the format shown below in the related table, with a NULL BrowserID stored in the Site Visitor Table if an unknown browser version accesses their website.
The client uses "Some Records Match" for the Referential Integrity setting because a match is not guaranteed.
The client wants to improve the performance of
the dashboard while also getting an accurate count of site visitors.
Which modifications to the data tables and join should the consultant recommend?
- A. Continue to use NULL as the BrowserID in the Site Visitor Table and change the Referential Integrity to
"All Records Match." - B. Add an "Unknown" option to the Browser Table, reference its BrowserID in the Site Visitor Table, and leave the Referential Integrity set to
"Some Records Match." - C. Add an "Unknown" option to the Browser Table, reference its BrowserID in the Site Visitor Table, and change the Referential Integrity to "All Records Match."
- D. Continue to use NULL as the BrowserID in the Site Visitor Table and leave the Referential Integrity set to "Some Records Match."
正解：C
解説：
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.
質問 # 30
A client wants to see data for only the last day in a dataset and the last day is always yesterday. The date is represented with the field Ship Date.
The client is not concerned about the daily refresh results. The volume of data is so large that performance is their priority. In the future, the client will be able to move the calculation to the underlying database, but not at this time.
The solution should offer the best performance.
Which approach should the consultant use to produce the desired results?
- A. Filter on Ship Date field using the Yesterday option.
- B. Filter on calculation [Ship Date]=TODAY()-1.
- C. Filter MONTH/DAY/YEAR on [Ship Date] field and use an option to filter to the latest date value when the workbook opens.
- D. Filter on calculation [Ship Date]={MAX([Ship Date])}.
正解：B
解説：
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.
質問 # 31
A client builds a dashboard that presents current and long-term stock measures. Currently, the data is at a daily level. The data presents as a bar chart that presents monthly results over current and previous years. Some measures must present as monthly averages.
What should the consultant recommend to limit the data source for optimal performance?
- A. Limit data to current and previous years as well as to the last day of each month to eliminate the need to use the averages.
- B. Limit data to current and previous years, move calculating averages to data layer, and aggregate dates to monthly level.
- C. Move calculating averages to data layer and aggregate dates to monthly level.
- D. Limit data to current and previous years and leave data at daily level to calculate the averages in the report.
正解：B
質問 # 32
A client notices that several groups are sharing content across divisions and are not complying with their data governance strategy. During a Tableau Server audit, a consultant notices that the asset permissions for the client's top-level projects are set to "Locked," but that "Apply to Nested Projects" is not checked.
The consultant recommends checking "Apply to Nested Projects" to enforce compliance.
Which impact will the consultant's recommendation have on access to the existing nested projects?
- A. Current custom access will be maintained, but new custom permissions will not be granted.
- B. Users will be prompted to manually update permissions for all nested projects.
- C. Users will be notified that they will automatically lose access to content after 30 days.
- D. Access will be automatically rolled back to the top-level project permissions immediately.
正解：D
解説：
When "Apply to Nested Projects" is checked in Tableau Server, the permission rules set at the top-level project are enforced for all assets in the project and all nested projects. This means that any custom access previously granted to nested projects will be overridden, and the permissions will revert to those defined at the top-level project. This action ensures consistent application of the data governance strategy across all divisions.
References:The impact of checking "Apply to Nested Projects" is detailed in Tableau's official documentation, which explains how locked nested projects can be used to govern site content with greater flexibility and efficiency12.
質問 # 33
A client wants to count all the distinct orders placed in 2010. They have written the following calculation, but the result is incorrect.
IF YEAR([Date])=2010 THEN COUNTD ([OrderID]) END
Which calculation will produce the correct result?
- A. IF MIN(YEAR([Date]))=2010 THEN WINDOW_COUNTD([OrderID]) END
- B. COUNTD(IF YEAR([Date])=2010 THEN [OrderID] END)
- C. COUNT(IF YEAR([Date])=2010 THEN [OrderID] END)
- D. IF YEAR([Date])=2010 THEN {COUNTD ([OrderID])} END
正解：B
解説：
The correct calculation to count all distinct orders placed in 2010 involves placing the conditional inside the aggregation function, not the other way around. Here's how to correct the client's calculation:
* Original Calculation Issue: The client's original calculation attempts to apply theCOUNTDfunction within anIFstatement, which does not work as expected because theCOUNTDfunction cannot conditionally count within the scope of theIFstatement.
* Correct Calculation:COUNTD(IF YEAR([Date]) = 2010 THEN [OrderID] END). This calculation checks each order date; if the year is 2010, it returns theOrderID. TheCOUNTDfunction then counts all uniqueOrderIDs that meet this condition.
* Why It Works: This method ensures that each order is first checked for the year condition before being counted, effectively filtering and counting in one step. It efficiently processes the data by focusing the distinct count operation only on relevant records.
ReferencesThis approach is consistent with Tableau's guidance on using conditional logic inside aggregation functions for accurate and efficient data calculations, as detailed in the Tableau User Guide under
"Aggregations and Calculations".
質問 # 34
A client builds a dashboard that presents current and long-term stock measures. Currently, the data is at a daily level. The data presents as a bar chart that presents monthly results over current and previous years. Some measures must present as monthly averages.
What should the consultant recommend to limit the data source for optimal performance?
- A. Limit data to current and previous years as well as to the last day of each month to eliminate the need to use the averages.
- B. Limit data to current and previous years, move calculating averages to data layer, and aggregate dates to monthly level.
- C. Move calculating averages to data layer and aggregate dates to monthly level.
- D. Limit data to current and previous years and leave data at daily level to calculate the averages in the report.
正解：B
解説：
For optimal performance, it is recommended to limit the data to what is necessary for analysis, which in this case would be the current and previous years. Moving the calculation of averages to the data layer and aggregating the dates to a monthly level will reduce the granularity of the data, thereby improving the performance of the dashboard.This approach aligns with best practices foroptimizing workbook performance in Tableau, which suggest simplifying the data model and reducing the number of records processed12.
References:The recommendation is based on the guidelines provided in Tableau's official documentation on optimizing workbook performance, which includes tips on data management and aggregation for better performance12.
質問 # 35
A consultant is designing a dashboard that will be consumed on desktops, tablets, and phones. The consultant needs to implement a dashboard design that provides the best user experience across all the platforms.
Which approach should the consultant take to achieve these results?
- A. Build one dashboard that has desktop, tablet, and phone layouts, and fix the size of the layouts.
- B. Build one dashboard for each type of device and fix the size of the layouts.
- C. Build one dashboard and set the size to Automatic.
- D. Build one dashboard and fix the size of the dashboard.
正解：A
解説：
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.
質問 # 36
A client is working in Tableau Prep and has a field named Orderld that is compiled by country, year, and an order number as shown in the following table.
What should the consultant use to transform the table in the most efficient manner?
- A. A calculated field that uses the TRIM function
- B. A calculated field that uses the LEFT function
- C. The Split option
- D. The Aliases option
正解：C
解説：
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.
質問 # 37
A client uses Tableau Data Management and notices that when they view a data source, they sometimes see a different count of workbooks in the Connected Workbooks tab compared to the lineage count in Tableau Catalog.
What is the cause of this discrepancy?
- A. Some Creators have connected to the data source in Tableau Desktop but have not yet published a workbook.
- B. Some workbooks have been connected to the data source, but do not use any fields from it.
- C. Some workbooks have not been viewed by enough users yet.
- D. Some of the workbooks connected to the data source are not visible to the user due to permissions.
正解：D
解説：
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.
質問 # 38
A consultant migrated a data source to improve performance. The consultant wants to identify which workbooks need to be updated to point to the new data source.
Which Tableau tool should the consultant use?
- A. Data Management
- B. Activity Log
- C. Prep Conductor
- D. Tableau Advanced Management
正解：D
解説：
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.
質問 # 39
A consultant builds a report where profit margin is calculated as SUM([Profit]) / SUM([Sales]). Three groups of users are organized on Tableau Server with the following levels of data access that they can be granted.
. Group 1: Viewers who cannot see any information on profitability
. Group 2: Viewers who can see profit and profit margin
. Group 3: Viewers who can see profit margin but not the value of profit Which approach should the consultant use to provide the required level of access?
- A. Use user filters to allow only Groups 2 and 3 access to data on profitability. Then, create a calculated field that limits visibility of profit value to Group 2 and use the calculation in the view in the report.
- B. Specify with user filters in each view individuals who can see profit, profit margin, or none of these.
- C. Use user filters to access data on profitability to all groups. Then, create a calculated field that allows visibility of profit value to Group 2 and use the calculation in the view in the report.
- D. Specify in the row-level security (RLS) entitlement table individuals who can see profit, profit margin, or none of these. Then, use the table data to create user filters in the report.
正解：A
解説：
The approach of using user filters to control access to data on profitability for Groups 2 and 3, combined with a calculated field that restricts the visibility of profit value to only Group 2, aligns with Tableau's best practices for managing content permissions. This method ensures that each group sees only the data they are permitted to view, with Group 1 not seeing any profitability information, Group 2 seeing both profit and profit margin, and Group 3 seeing only the profit margin without the actual profit values.This setup can be achieved through Tableau Server's permission capabilities, which allow for detailed control over what each user or group can see and interact with12.
References:The solution is based on the capabilities and permission rules that are part of Tableau Server's security model, as detailed in the official Tableau documentation12. These resources provide guidance on how to set up user filters and calculated fields to manage data access levels effectively.
質問 # 40
A client wants to produce a visualization to show quarterly profit growth and aggregated sales totals across a number of product categories from the data provided below.
Which set of charts should the consultant use to meet the client's requirements?
- A. Scatter plot and pie chart
- B. Gantt and bar charts
- C. Line and bubble charts
- D. Waterfall chart and tree map
正解：D
解説：
To effectively display quarterly profit growth and aggregated sales totals across different product categories, a combination of a Waterfall chart and a Tree Map is recommended:
* Waterfall Chart: This chart type is excellent for visualizing the sequential growth or decline of profits across different quarters for each sub-category. It clearly shows how profits accumulate over time, highlighting both positive and negative changes, which makes it ideal for tracking profit growth or decline through the quarters.
* Tree Map: A Tree Map can efficiently display aggregated sales totals where each block size represents the total sales of a product category, providing a quick, visually impactful comparison across categories.
This is especially useful when the client wants to understand which categories contribute most to sales in a glanceable format.
Together, these charts provide a comprehensive overview of both profit trends over time (Waterfall Chart) and a comparative snapshot of sales performance across categories (Tree Map), meeting the client's need to analyze performance dynamics in a detailed yet consolidated manner.
ReferencesThese recommendations are based on common best practices for data visualization in Tableau, where specific chart types are chosen for their strengths in communicating certain types of data relationships and dynamics, as detailed in Tableau's official visualization guides.
質問 # 41
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