最新 [2025年01月06日]TCC-C01試験正確解答Tableau Certified ConsultantのPDF問題 [Q20-Q40]

最新 [2025年01月06日]TCC-C01試験正確解答Tableau Certified ConsultantのPDF問題

あなたのキャリアーを稼いで飛躍せよTableau 57問題

質問 # 20
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 in each view of each report using set filters and option Server/Create User Filter.
• 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 based on data policies and apply them to a published data source.

正解：D

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 # 21
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. Hide container with all quick filters with a Show/Hide Button.
• D. 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.

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 # 22
An online sales company has a table data source that contains Order Date. Products ship on the first day of each month for all orders from the previous month.
The consultant needs to know the average number of days that a customer must wait before a product is shipped.
Which calculation should the consultant use?

• A. Calc1: DATETRUNC ('month', DATEADD ('month', 1, [Order Date]))
  Calc2: AVG(DATEDIFF ('day', [Order Date], [Calc1]))
• B. Calc1: DATETRUNC ('day', DATEADD ('day', 31, [Order Date]))
  Calc2: AVG ([Order Date] - [Calc1])
• C. Calc1: DATETRUNC ('month', DATEADD('month', 1, [Order Date]))
  Calc2: AVG(DATEDIFF ('week', [Order Date], [Calc1]))
• D. Calc1: DATETRUNC ('day', DATEADD('week', 4, [Order Date]))
  Calc2: AVG([Order Date] - [Calc1])

正解：A

解説：
The correct calculation to determine the average number of days a customer must wait before a product is shipped is to first find the shipping date, which is the first day of the following month after the order date. This is done usingDATETRUNC('month', DATEADD('month', 1, [Order Date])). Then, the average difference in days between the order date and the shipping date is calculated usingAVG(DATEDIFF('day', [Order Date],
[Calc1])). This approach ensures that the average wait time is calculated in days, which is the most precise measure for this scenario.
References:The solution is based on Tableau's date functions and their use in calculating differences between dates, which are well-documented in Tableau's official learning resources and consultant documents12.
To calculate the average waiting days from order placement to shipping, where shipping occurs on the first day of the following month:
* Calculate Shipping Date (Calc1): Use the DATEADD function to add one month to the order date, then apply DATETRUNC to truncate this date to the first day of that month. This represents the shipping date for each order.
* Calculate Average Wait Time (Calc2): Use DATEDIFF to calculate the difference in days between the original order date and the calculated shipping date (Calc1). Then, use AVG to average these differences across all orders, giving the average number of days customers wait before their products are shipped.
References:
* Date Functions in Tableau: Functions like DATEADD, DATETRUNC, and DATEDIFF are used to manipulate and calculate differences between dates, crucial for creating metrics that depend on time intervals, such as customer wait times in this scenario.

質問 # 23
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. COUNTD(IF YEAR([Date])=2010 THEN [OrderID] END)
• B. COUNT(IF YEAR([Date])=2010 THEN [OrderID] END)
• C. IF YEAR([Date])=2010 THEN {COUNTD ([OrderID])} END
• D. IF MIN(YEAR([Date]))=2010 THEN WINDOW_COUNTD([OrderID]) END

正解：A

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 # 24
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. Set up the extract as an incremental refresh.
• C. Define the filters by using custom SQL.
• D. Aggregate the extract to the visible dimensions.

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 # 25
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 # 26
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. Access will be automatically rolled back to the top-level project permissions immediately.
• B. Current custom access will be maintained, but new custom permissions will not be granted.
• C. Users will be prompted to manually update permissions for all nested projects.
• D. Users will be notified that they will automatically lose access to content after 30 days.

正解：A

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 # 27
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 calculation [Ship Date]=TODAY()-1.
• B. Filter on calculation [Ship Date]={MAX([Ship Date])}.
• 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 Ship Date field using the Yesterday option.

正解：A

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 # 28
A client is searching for ways to curate and document data in order to obtain data lineage. The client has a data source connected to a data lake.
Which tool should the consultant recommend to meet the client's requirements?

• A. Tableau Catalog with Tableau Data Management Add-on
• B. Tableau Catalog without Tableau Data Management Add-on
• C. Tableau Catalog with Tableau Server Management Add-on
• D. Tableau Prep Conductor

正解：A

解説：
To effectively curate and document data for obtaining data lineage, particularly from a data source connected to a data lake, the recommended tool is:
* Tableau Catalog with Tableau Data Management Add-on: This add-on enhances the capabilities of Tableau Catalog, providing extensive features for data management, including detailed data lineage, impact analysis, and metadata management.
* Functionality: The Tableau Catalog with the Data Management Add-on allows users to see the full history and lineage of the data, trace its usage across all Tableau content, and understand dependencies.
It also facilitates better governance and transparency in data handling.
* Why Choose this Tool: For a client needing comprehensive data lineage and documentation capabilities, this add-on ensures that data stewards and users can maintain and utilize a well-managed data environment. It supports robust data governance practices necessary for large and complex data ecosystems like those typically associated with data lakes.
ReferencesThe recommendation is based on the functionalities offered by the Tableau Data Management Add-on, as described in Tableau's official documentation on managing and documenting data sources for enhanced governance and operational efficiency.

質問 # 29
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 Desktop to visualize null values.
• C. Update the Widget Inventory Table to be a daily snapshot.
• D. Use Tableau Prep to add new rows.

正解：D

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 # 30
A client has several long-term shipping contracts with different vendors that set rates based on shipping volume and speed. The client requests a dashboard that allows them to model shipping costs for the next week based on the selected shipping vendor. Speed for the end user is critical.
Which dashboard building strategy will deliver the desired result?

• A. Recommend that the client model for only profitability for the next 24 hours instead of a full week.
• B. Use a calculated field that refers to a user-selected parameter to calculate shipping costs for each order and then display the aggregate values.
• C. Aggregate the orders then use a calculated field that refers to a user-selected parameter to calculate the shipping costs.
• D. Calculate the potential shipping cost for each order with each vendor, display the aggregate costs in a large table, and use quick filters to limit the options visible to the user.

正解：B

解説：
For modeling shipping costs based on varying vendor contracts and ensuring speed in dashboard performance, the suggested approach involves:
* Calculated Field with Parameter: Utilize a calculated field that dynamically references a user-selected parameter for the shipping vendor. This parameter adjusts the cost calculations based on selected vendor characteristics (like volume and speed).
* Aggregate Results: After calculating individual shipping costs, aggregate these costs to provide a concise, summarized view of potential expenses for the upcoming week. This method ensures the dashboard remains performant by reducing the load of processing individual line items in real-time.
* Why This Works: By using parameters and calculated fields, the dashboard can quickly adapt to user inputs without needing to re-query the entire dataset. Aggregating the results further improves performance and user experience by simplifying the output.
ReferencesThis strategy leverages Tableau's capability to handle dynamic calculations with parametersand is recommended for scenarios where performance and user-driven interaction are priorities. Tableau's performance optimization resources and dashboard design guidelines detail these techniques.

質問 # 31
A client is using Tableau to visualize data by leveraging security token-based credentials. Suddenly, sales representatives in the field are reporting that they cannot access the necessary workbooks. The client cannot recreate the error from their offices, but they have seen screenshots from the field agents. The client wants to restore functionality for the field agents with minimal disruption.
Which step should the consultant recommend to accomplish the client's goal?

• A. Change the data source permissions for the connection to "Prompt User."
• B. Ask the workbook owners to republish the workbooks to refresh the security token.
• C. Ensure that "Allow Refresh Access" was checked when the data source was published.
• D. Renew the security token via the Data Connection on Tableau Server.

正解：D

解説：
When field agents are unable to access workbooks due to issues with security token-based credentials, the most immediate and least disruptive solution is to renew the security token. This can be done through the Data Connection settings on Tableau Server. Renewing the token will restore access for the field agents without requiring them to take any action or affecting other users.
References:The use of personal access tokens (PATs) in Tableau and the procedure for renewing them are documented in Tableau's official resources.It is noted that PATs are long-lived authentication tokens that can be revoked and renewed to manage access securely1.Additionally, there have been discussions in the Tableau Community regarding issues with concurrent PAT access, which further supports the need to manage tokens effectively2.

質問 # 32
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. Relating the tables in Tableau Desktop
• B. Joining the tables in Tableau Prep
• C. Blending the first two tables with the third
• D. Joining the tables in Tableau Desktop

正解：A

質問 # 33
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 # 34
A client wants to migrate their Tableau Server to Tableau Cloud. The Tableau Server is configured with three sites: Finance, Strategy, and Marketing. A consultant must provide a solution that minimizes user impact and costs.
Which configuration should the consultant recommend for Tableau Cloud to meet the client's requirements?

• A. One Tableau Cloud instance configured with all workbooks in a single project
• B. Three separate Tableau Cloud instances for Finance, Strategy, and Marketing
• C. One Tableau Cloud instance configured with a Finance project folder, Strategy project folder, and Marketing project folder
• D. One Tableau Cloud instance with two sites for Strategy and Marketing, and one Tableau Server instance for Finance

正解：C

解説：
To minimize user impact and costs while migrating from Tableau Server to Tableau Cloud with multiple sites, the best solution is:
* Single Tableau Cloud Instance with Multiple Projects: Instead of multiple sites which could imply higher management overhead and possibly higher costs, configuring one Tableau Cloud instance with different project folders for each former site (Finance, Strategy, Marketing) is most efficient.
* Benefits: This setup maintains organizational separation of data and access similar to having different sites but leverages the unified management and simplicity of a single cloud instance. It reduces complexity in user access management and integration points.
* Implementation: Each project folder acts like a mini-site within the larger instance, where specific permissions and content can be managed independently, akin to the original server setup but within a single cloud-based environment.
ReferencesThis recommendation is in line with best practices for cloud migration focusing on consolidation and cost efficiency, as suggested in Tableau's official documentation for cloud migration strategies.

質問 # 35
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. Use join option to combine/merge all the files together before doing the data transformation (calculations).
• B. Use wildcard Union option to combine/merge all the files together before doing the data transformation (calculations).
• C. Apply the data transformation (calculations) in each require file and do the join to combine/merge before designing the visuals.
• D. Apply the data transformation (calculations) in each require file and do the wildcard union to combine/merge before designing the visuals.

正解：B

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 # 36
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. Quick Table Calculation
• B. MAX() Function
• C. Level of Detail (LOD) Calculation
• D. A Window Function

正解：C

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 # 37

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 # 38
A client has a pipeline dashboard that takes a long time to load. The dashboard is connected to only one large data source that is an extract.
It contains two calculated fields:
. TOTAL([Opportunities])
SUM([Value])
It also contains two filters:
. A Relative Date filter on Created Date, a Date field containing values from 5 years ago until today
. A Multiple Values (Dropdown) filter on Account Name, a String field containing 1,000 distinct values A consultant creates a Performance Recording to troubleshoot the issue, and finds out that the longest-running event is "Executing Query." Which step should the consultant take to resolve this issue?

• A. Replace SUM([Value]) with WINDOW_SUM([Value]).
• B. Replace the Multiple Values (Dropdown) filter with a Multiple Values (Custom List) filter.
• C. Replace the TOTAL([Opportunities]) calculation with a Grand Total.
• D. Replace the Relative Date filter with a Multiple Values (Dropdown) filter on YEAR([Created Date]).

正解：D

解説：
To improve the loading time of the pipeline dashboard, which primarily suffers from long query execution times due to a comprehensive Relative Date filter:
* Relative Date Filter Issue: The existing Relative Date filter on "Created Date" covers a broad range (5
* years), leading to significant data processing overhead as it includes granular date calculations over a large dataset.
* Optimized Approach: By replacing the Relative Date filter with a Multiple Values (Dropdown) filter based on YEAR([Created Date]), the filter granularity is reduced. Filtering by year simplifies the query by limiting the volume of data processed and reducing the complexity of the filter condition.
* Implementation Benefit: This approach still provides the flexibility to view data across different years but does so by reducing the load on the database during query execution, which is critical for improving the performance of the dashboard.
ReferencesThis recommendation aligns with Tableau performance optimization strategies, specifically regarding the management of date filters to minimize their impact on query load, as discussed in Tableau performance tuning sessions and guides.

質問 # 39
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 # 40
......

正真正銘のベスト資料はTCC-C01オンライン練習試験：https://jp.fast2test.com/TCC-C01-premium-file.html

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