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質問 # 24
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 to a different project.
- B. New credentials were embedded into the data source.
- C. The dashboard was published with a new name.
- D. Tableau Server was upgraded.
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 # 25
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 # 26
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 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.
- B. 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.
- C. Specify with user filters in each view individuals who can see profit, profit margin, or none of these.
- D. 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.
正解：D
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 # 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. 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.
- B. Use Dynamic Zone Visibility to inform viewers which quick filters apply to warehouses or shops.
- C. 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.
- D. Hide container with all quick filters with a Show/Hide Button.
正解：C
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 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 MONTH/DAY/YEAR on [Ship Date] field and use an option to filter to the latest date value when the workbook opens.
- B. Filter on calculation [Ship Date]=TODAY()-1.
- C. Filter on calculation [Ship Date]={MAX([Ship Date])}.
- D. Filter on Ship Date field using the Yesterday option.
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 # 29
An executive-level workbook leverages 37 of the 103 fields included in a data source. Performance for the workbook is noticeably slower than other workbooks on the same Tableau Server.
What should the consultant do to improve performance of this workbook while following best practice?
- A. Restrict users from accessing the workbook to reduce server load.
- B. Split some visualizations on the dashboard into many smaller visualizations on the same dashboard.
- C. Connect to the data source via a custom SQL query.
- D. Use filters, hide unused fields, and aggregate values.
正解：D
解説：
To improve the performance of a Tableau workbook, it is best practice to streamline the data being used. This can be achieved by using filters to limit the data to only what is necessary for analysis, hiding fields that are not being used to reduce the complexity of the data model, and aggregating values to simplify the data and reduce the number of rows that need to be processed. These steps can help reduce the load on the server and improve the speed of the workbook.
References:The best practices for optimizing workbook performance in Tableau are well-documented in Tableau's official resources, including the Tableau Help Guide and the Designing Efficient Workbooks whitepaper, which provide detailed recommendations on how to streamline workbooks for better performance12.
質問 # 30
A client has a database that stores widget inventory by day and it is updated on a nonstandard schedule as shown below.
They want a data visualization that shows widget inventory daily, however their business unit does not have the ability to modify the data warehouse structure.
What should the client do to achieve the desired result?
- A. Create a temporary table in the database.
- B. Use Tableau Prep to add new rows.
- C. Update the Widget Inventory Table to be a daily snapshot.
- D. Use Tableau Desktop to visualize null values.
正解：B
解説：
For a client who needs a daily visualization of widget inventory but cannot modify the data warehouse structure, the best approach is to use Tableau Prep to add new rows. Tableau Prep can be used to manipulate the existing dataset by adding missing date entries and appropriately adjusting inventory counts based on available data. This allows the creation of a complete daily snapshot for visualization without needing changes to the underlying database structure.
質問 # 31
A client is considering migrating from Tableau Server to Tableau Cloud.
Which two elements are determining factors of whether the client should use Tableau Server or Tableau Cloud? Choose two.
- A. Whether or not the client plans to leverage single sign-on (SSO)
- B. Whether or not the client needs the ability to connect to public, cloud-based data sources
- C. Whether or not there are large numbers of concurrent extract refreshes
- D. Amount of data storage used on the client's existing server
正解：A、C
解説：
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.
質問 # 32
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 # 33
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 based on data policies and apply them to views using set filters and option Server/Create User Filter.
- D. Create User filters in each view of each report 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 # 34
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 perform a full extract refresh every Saturday.
- B. Publish a live connection to Tableau Server.
- C. Publish an incremental refresh every Saturday.
- D. Publish an incremental extract refresh every day and publish a secondary data set containing data revisions.
正解：A
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 # 35
From the desktop, open the CC workbook.
Open the Incremental worksheet.
You need to add a line to the chart that
shows the cumulative percentage of sales
contributed by each product to the
incremental sales.
From the File menu in Tableau Desktop, click
Save.
正解：
解説：
See the complete Steps below in Explanation:
Explanation:
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:
* From the desktop, double-click on the CC workbook to open it in Tableau Desktop.
* Navigate to the Incremental worksheet by clicking on its tab at the bottom of the window.
* Calculate Cumulative Sales Percentage:
* Create a new calculated field to compute the cumulative percentage of sales. Right-click in the Data pane and select 'Create Calculated Field'.
* Name this field "Cumulative Sales Percentage".
* Enter the following formula to calculate the running sum of sales as a percentage of the total sales:
(RUNNING_SUM(SUM([Sales])) / TOTAL(SUM([Sales])) [Sales]))
* Click 'OK' to save the calculated field.
* Add the Cumulative Sales Percentage Line to the Chart:
* Drag the "Cumulative Sales Percentage" field to the Rows shelf, placing it next to the existing Sales measure.
* Ensure that the cumulative line appears as a continuous line. Right-click on the "Cumulative Sales Percentage" field on the Rows shelf, select 'Change Chart Type', and choose 'Line'.
* Adjust the axis to synchronize or dual-axis if necessary. Right-click on the axis of the
"Cumulative Sales Percentage" and select 'Synchronize Axis' if it's on a dual-axis setup.
* Format the Cumulative Sales Percentage Line:
* Click on the "Cumulative Sales Percentage" line in the visualization.
* Navigate to the 'Format' pane to adjust the line style, thickness, and color to make it distinct from other data in the chart.
* Save Your Changes:
* From the File menu, click 'Save' to ensure all your changes are stored.
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.
質問 # 36
A client has many published data sources in Tableau Server. The data sources use the same databases and tables. The client notices different departments give different answers to the same business questions, and the departments cannot trust the data. The client wants to know what causes data sources to return different data.
Which tool should the client use to identify this issue?
- A. Ask Data
- B. Tableau Prep Conductor
- C. Tableau Resource Monitoring Tool
- D. Tableau Catalog
正解：D
解説：
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.
質問 # 37
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. Use a calculation with TOTAL() function instead of a quick table calculation.
- B. Filter with a table calculation WINDOW_AVG(MIN([Profit]), first(), last())
- 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 # 38
A client currently has a workbook with the table shown below.
Which method will produce the output for the Total Sales Value field for all the categories shown in the table?
- A. Level of Detail (LOD) Calculation
- B. A Window Function
- C. MAX() Function
- D. Quick Table Calculation
正解：A
解説：
To calculate the Total Sales Value for all categories as displayed in the table, an LOD expression is ideal. An LOD calculation in Tableau allows you to compute values at the data level that is different from the view level. In this case, since the Total Sales Value appears consistent across different sub-categories within each category, an LOD expression can be used to fix the Total Sales Value irrespective of the sub-category detail.
Here's how to set it up:
* Go to the Calculations area by right-clicking in the data pane and selecting "Create Calculated Field".
* Enter a name for the calculation, such as "Total Sales Value".
* Enter the LOD expression:{ FIXED [Category] : SUM([Sales]) }. This calculation fixes the total sales to the category level, effectively summing sales for all sub-categories within each category, irrespective of how the data is broken down in the view.
* Drag this new calculated field into your visualization alongside the existing measures.
This method ensures that the Total Sales Value reflects the total for each category across all its sub-categories, matching the uniform values shown across different rows for each category in your table.
ReferencesThe explanation utilizes the concept of Level of Detail calculations in Tableau, which allows for advanced aggregations independent of the view level details. This concept is covered extensively in Tableau's official documentation and relevant training materials such as Tableau's online help resources.
質問 # 39
A client wants to see the average number of orders per customer per month, broken down by region. The client has created the following calculated field:
Orders per Customer: {FIXED [Customer ID]: COUNTD([Order ID])}
The client then creates a line chart that plots AVG(Orders per Customer) over MONTH(Order Date) by Region. The numbers shown by this chart are far higher than the customer expects.
The client asks a consultant to rewrite the calculation so the result meets their expectation.
Which calculation should the consultant use?
- A. {FIXED [Customer ID], [Region]: COUNTD([Order ID])}
- B. {INCLUDE [Customer ID]: COUNTD([Order ID])}
- C. {FIXED [Customer ID], [Region], [Order Date]: COUNTD([Order ID])}
- D. {EXCLUDE [Customer ID]: COUNTD([Order ID])}
正解：A
解説：
The calculation{FIXED [Customer ID], [Region]: COUNTD([Order ID])}is the correct one to use for this scenario. This Level of Detail (LOD) expression will calculate the distinct count of orders for each customer within each region, which is then averaged per month. This approach ensures that the average number of orders per customer is accurately calculated for each region and then broken down by month, aligning with the client's expectations.
References:The LOD expressions in Tableau allow for precise control over the level of detail at which calculations are performed, which is essential for accurate data analysis.The use of{FIXED}expressions to specify the granularity of the calculation is a common practice and is well-documented in Tableau's official resources12.
The initial calculation provided by the client likely overestimates the average number of orders per customer per month by region due to improper granularity control. The revised calculation must take into account both the customer and the region to correctly aggregate the data:
* FIXED Level of Detail Expression: This calculation uses a FIXED expression to count distinct order IDs for each customer within each region. This ensures that the count of orders is correctly grouped by both customer ID and region, addressing potential duplication or misaggregation issues.
* Accurate Aggregation: By specifying both [Customer ID] and [Region] in the FIXED expression, the calculation prevents the overcounting of orders that may appear if only customer ID was considered, especially when a customer could be ordering from multiple regions.
References:
* Level of Detail Expressions in Tableau: These expressions allow you to specify the level of granularity you need for your calculations, independent of the visualization's level of detail, thus offering precise control over data aggregation.
質問 # 40
A client calculates the percent of total sales for a particular region compared to all regions.
Which calculation will fix the automatic recalculation on the % of total field?
- A. {FIXED [Region]:sum([Sales])}
- B. {FIXED [Region]:sum([Sales])}/SUM([Sales]}
- C. {FIXED [Region]:sum([Sales])}/{FIXED :SUM([Sales])
- D. {FIXED [Region]:[Sales]}/{FIXED: SUM([Sales])}
正解：B
解説：
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.
ReferencesThis 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.
質問 # 41 
From the desktop, open the NYC
Property Transactions workbook.
You need to record the performance of
the Property Transactions dashboard in
the NYC Property Transactions.twbx
workbook. Ensure that you start the
recording as soon as you open the
workbook. Open the Property
Transactions dashboard, reset the filters
on the dashboard to show all values, and
stop the recording. Save the recording in
C:\CC\Data\.
Create a new worksheet in the
performance recording. In the worksheet,
create a bar chart to show the elapsed
time of each command name by
worksheet, to show how each sheet in
the Property Transactions dashboard
contributes to the overall load time.
From the File menu in Tableau Desktop,
click Save. Save the performance
recording in C:\CC\Data\.
正解：
解説：
See the complete Steps below in Explanation:
Explanation:
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:
* From the desktop, double-click the NYC Property Transactions.twbx workbook to open it in Tableau Desktop.
* Start Performance Recording:
* Before doing anything else, navigate to the 'Help' menu in Tableau Desktop.
* Select 'Settings and Performance', then choose 'Start Performance Recording'.
* Open the Property Transactions Dashboard and Reset Filters:
* Navigate to the Property Transactions dashboard within the workbook.
* Reset all filters to show all values. This usually involves selecting the dropdown on each filter and choosing 'All' or using a 'Reset' button if available.
* Stop the Performance Recording:
* Go back to the 'Help' menu.
* Choose 'Settings and Performance', then select 'Stop Performance Recording'.
* Tableau will automatically open a new tab displaying the performance recording results.
* Save the Performance Recording:
* In the performance recording results tab, go to the 'File' menu.
* Click 'Save As' and navigate to the C:\CC\Data\ directory.
* Save the file, ensuring it is stored in the desired location.
* Create a New Worksheet for Performance Analysis:
* Return to the NYC Property Transactions workbook and create a new worksheet by clicking on the 'New Worksheet' icon.
* Drag the 'Command Name' field to the Columns shelf.
* Drag the 'Elapsed Time' field to the Rows shelf.
* Ensure that the 'Worksheet' field is also included in the analysis to break down the time by individual sheets within the dashboard.
* Choose 'Bar Chart' from the 'Show Me' options to display the data as a bar chart.
* Customize and Finalize the Bar Chart:
* Adjust the axes and labels to clearly display the information.
* Format the chart to enhance readability, applying color coding or sorting as needed to emphasize sheets with longer load times.
* Save Your Work:
* Once the new worksheet and the performance recording are complete, ensure all work is saved.
* Navigate to the 'File' menu and click 'Save', confirming that changes are stored in the workbook.
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.
質問 # 42
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. 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."
- 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. Continue to use NULL as the 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：A
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 # 43
From the desktop, open the CCworkbook. Use the US PopulationEstimates data source.
You need to shape the data in USPopulation Estimates by using TableauDesktop. The data must be formatted asshown in the following table.
Open the Population worksheet. Enterthe total number of records contained inthe data set into the Total Recordsparameter.
From the File menu in Tableau Desktop,click Save.
正解：
解説：
See the complete Steps below in Explanation:
Explanation:
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:
* From the desktop, double-click on the CC workbook to open it in Tableau Desktop.
* Navigate to the Population worksheet by selecting its tab at the bottom of the window.
* Format and Shape the Data:
* Ensure the data types match those specified in the requirements: Sex, Origin, Race as strings; Year, Age, Population as whole numbers.
* To verify or change the data type, click on the dropdown arrow next to each field name in the Data pane and select "Change Data Type" if necessary.
* Calculate Total Number of Records:
* Create a new calculated field named "Total Records". To do this, right-click in the Data pane and select "Create Calculated Field".
* Enter the formulaCOUNT([Record ID])orSUM([Number of Records])depending on how the data source identifies each row uniquely.
* Drag this new calculated field onto the worksheet to display the total number of records.
* Enter the Value into the Total Records Parameter:
* Locate the "Total Records" parameter in the Data pane. Right-click on the parameter and select
"Edit".
* Manually enter the number displayed from the calculated field into the parameter, ensuring accuracy to meet the data shaping requirement.
* Save Your Changes:
* From the File menu, click 'Save' to ensure all your changes are stored.
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.
質問 # 44
Use the following login credentials to sign in
to the virtual machine:
Username: Admin
Password:
The following information is for technical
support purposes only:
Lab Instance: 40201223
To access Tableau Help, you can open the
Help.pdf file on the desktop.
From the desktop, open the CC workbook.
Open the Categorical Sales worksheet.
You need to use table calculations to
compute the following:
. For each category and year, calculate
the average sales by segment.
. Create another calculation to
compute the year-over-year
percentage change of the average
sales by category calculation. Replace
the original measure with the year-
over-year percentage change in the
crosstab.
From the File menu in Tableau Desktop, click
Save.
正解：
解説：
See the complete Steps below in Explanation:
Explanation:
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:
* Open the CC workbook and navigate to the Categorical Sales worksheet.
* Drag the 'Sales' field to the Rows shelf if it's not already there.
* Drag the 'Segment' field to the Rows shelf as well, placing it next to 'Category' and 'Year'.
* Right-click on the 'Sales' field in the Rows shelf and select 'Quick Table Calculation' > 'Average'.
This will compute the average sales for each segment within each category and year.
* Create a Calculation for Year-over-Year Percentage Change:
* Right-click in the data pane and select 'Create Calculated Field'.
* Name the calculated field something descriptive, e.g., "YoY Sales Change".
* Enter the formula to calculate the year-over-year percentage change:
(ZN(SUM([Sales])) - LOOKUP(ZN(SUM([Sales])), -1)) / ABS(LOOKUP(ZN(SUM([Sales])), -1))
* Click 'OK' to save the calculated field.
* Replace the Original Measure with the Year-over-Year Percentage Change in the Crosstab:
* Remove the original 'Sales' measure from the view by dragging it off the Rows shelf.
* Drag the newly created "YoY Sales Change" calculated field to the Rows shelf where the 'Sales' field was originally.
* Format the "YoY Sales Change" field to display as a percentage. Right-click on the field in the Rows shelf, select 'Format', and adjust the number format to percentage.
* Save Your Changes:
* From the File menu, click 'Save' to ensure all your changes are stored.
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.
質問 # 45
A client requests a published Tableau data source that is connected to SQL Server. The client needs to leverage the multiple tables option to create an extract. The extract will include partial data from the SQL Server data source.
Which action will reduce the amount of data in the extract?
- A. Use an extract filter.
- B. Define the filters by using custom SQL.
- C. Aggregate the extract to the visible dimensions.
- D. Set up the extract as an incremental refresh.
正解：A
解説：
Using an extract filter is an effective way to reduce the amount of data in a Tableau extract. Extract filters allow you to specify a subset of the data to include, which can significantly decrease the size of the extract by excluding unnecessary data. This is particularly useful when you only need partial data from a larger SQL Server data source.
References:The recommendation to use extract filters to reduce data size is supported by Tableau's best practices for optimizing extracts.These practices suggest keeping the extract's data set short through filtering1.Additionally, discussions in the Tableau Community confirm that hiding fields and using extract filters before extracting data can help reduce the extract size2.
When dealing with large datasets in SQL Server and needing to create a manageable extract in Tableau, using an extract filter is the most direct and effective method to limit the data included:
* Extract Filter: This involves setting filters that apply directly when the data is extracted from the source. This means that only the data meeting the specified criteria will be extracted and loaded into Tableau, significantly reducing the size of the extract.
* To apply an extract filter, in the Data Source page in Tableau, drag the fields you want to filter by to the Filters shelf. Then, configure the desired filter criteria. When you create the extract, choose the option to
* "Add Filters to Extract" and select the configured filters. This ensures that only the data that meets these conditions is extracted from the SQL Server.
This approach not only minimizes the data volume but also speeds up performance in Tableau because it processes a smaller subset of the full dataset.
ReferencesThis procedure is described in detail in Tableau's help documentation on managing extracts and optimizing performance by using extract filters, which is recommended for scenarios involving large datasets or when specific subsets of data are required for analysis.
質問 # 46
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