[2025年更新]合格できるTCC-C01試験にはリアルな問題解答
TCC-C01試験問題ゲット最新[2025]と正解回答
質問 # 24
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. Waterfall chart and tree map
- B. Gantt and bar charts
- C. Scatter plot and pie chart
- D. Line and bubble charts
正解：A
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 # 25
A client has a published data source in Tableau Server and they want to revert to the previous version of the data source. The solution must minimize the impact on users.
What should the consultant do to accomplish this task?
- A. Request that a server administrator restore a Tableau Server backup.
- B. Delete and recreate the data source manually.
- C. Select a previous version from Tableau Server, and then click Restore.
- D. Select a previous version from Tableau Server, download it, and republish that data source.
正解：C
解説：
To minimize the impact on users when reverting to a previous version of a published data source in Tableau Server, the consultant should use the built-in revision history feature. By selecting a previous version from the revision history and clicking 'Restore', the data source will revert to that version without the need for a full server backup restoration or manual recreation of the data source. This process is quick and has the least amount of disruption to users.
References:The functionality and process for reverting to a previous version of a data source are outlined in Tableau's official documentation on working with content revisions1.This feature is part of Tableau Server's capabilities to manage and maintain data sources effectively21.
質問 # 26
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 # 27
A client wants to provide sales users with the ability to perform the following tasks:
Access published visualizations and published data sources outside the company network.
Edit existing visualizations.
Create new visualizations based on published data sources.
. Minimize licensing costs.
Which site role should the client assign to the sales users?
- A. Explorer (can publish)
- B. Creator
- C. Viewer
- D. Site Administrator
正解：A
解説：
The Explorer (can publish) site role in Tableau is designed for users who need to access, edit, and create visualizations based on published data sources, even when they are outside the company network. This role allows users to perform web editing and save their work, making it suitable for sales users who need these capabilities. It is also a cost-effective option as it does not require the full capabilities and associated costs of the Creator license.
References:The information about the Explorer (can publish) role and its capabilities can be found in the official Tableau documentation on site roles and permissions12.This role is appropriate for users who need to interact with published content and create new visualizations without the need for full site administration or advanced content creation tools that come with the Creator role3.
質問 # 28
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. Tableau Catalog
- B. Tableau Resource Monitoring Tool
- C. Tableau Prep Conductor
- D. Ask Data
正解：A
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 # 29
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. Aggregate the extract to the visible dimensions.
- C. Set up the extract as an incremental refresh.
- D. Define the filters by using custom SQL.
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 # 30
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 a live connection to Tableau Server.
- C. Publish an incremental extract refresh every day and perform a full extract refresh every Saturday.
- D. Publish an incremental refresh every Saturday.
正解：C
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 # 31
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. Three separate Tableau Cloud instances for Finance, Strategy, and Marketing
- B. One Tableau Cloud instance with two sites for Strategy and Marketing, and one Tableau Server instance for Finance
- C. One Tableau Cloud instance configured with all workbooks in a single project
- D. One Tableau Cloud instance configured with a Finance project folder, Strategy project folder, and Marketing project folder
正解：D
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 # 32
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 # 33
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. {INCLUDE [Customer ID]: COUNTD([Order ID])}
- B. {EXCLUDE [Customer ID]: COUNTD([Order ID])}
- C. {FIXED [Customer ID], [Region], [Order Date]: COUNTD([Order ID])}
- D. {FIXED [Customer ID], [Region]: COUNTD([Order ID])}
正解：D
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 # 34
A consultant wants to improve the performance of reports by moving calculations to the data layer and materializing them in the extract.
Which calculation should the consultant use?
- A. CASE [Sector Parameter]
WHEN 1 THEN "green"
WHEN 2 THEN "yellow" - B. POWER(ZN(SUM([Sales]))/
LOOKUP(ZN(SUM([Sales])), FIRST()),ZN(1/(INDEX()-1)))
- 1
END - C. SUM([Profit])/SUM([Sales])
- D. ZN([Sales])*(1 - ZN([Discount]))
正解：C
解説：
To improve performance by moving calculations to the data layer and materializing them in the extract, the consultant should choose calculations that benefit from pre-computation and significantly reduce the load during query time:
* Aggregation-Level Calculation: The formula SUM([Profit])/SUM([Sales]) calculates a ratio at an aggregate level, which is ideal for pre-computation. Materializing this calculation in the extract means that the complex division operation is done once and stored, rather than being recalculated every time the report is accessed.
* Performance Improvement: By pre-computing this aggregate ratio, Tableau can utilize the pre-calculated fields directly in visualizations, which speeds up report loading and interaction times as the heavy lifting of data processing is done during the data preparation stage.
References:
* Materialization in Extracts: This concept involves pre-calculating and storing complex aggregations or calculations within the Tableau data extract itself, improving performance by reducing the computational load during visualization rendering.
質問 # 35
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. New credentials were embedded into the data source.
- B. Tableau Server was upgraded.
- C. The dashboard was published with a new name.
- D. The dashboard was published to a different project.
正解：C、D
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 # 36
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. Move calculating averages to data layer and aggregate dates to monthly level.
- C. Limit data to current and previous years and leave data at daily level to calculate the averages in the report.
- D. Limit data to current and previous years, move calculating averages to data layer, and aggregate dates to monthly level.
正解：D
質問 # 37
A consultant plans a Tableau deployment for a client that uses Salesforce. The client wants users to automatically see Tableau views of regional sales filtered by customer as soon as the users sign into Salesforce.
Which approach should the consultant use to deliver the final visualization?
- A. Embed views into Salesforce.
- B. Create subscriptions for each view to deliver reports by email.
- C. Create a list of URLs that the users can click in Salesforce.
- D. Publish to Tableau Mobile for viewing.
正解：A
解説：
To ensure that users automatically see Tableau views of regional sales filtered by customer as they sign into Salesforce, embedding the views directly into Salesforce is most effective:
* Embedding Views: Tableau provides capabilities to embed its dashboards into web applications such as Salesforce. This approach ensures that the visualization is part of the Salesforce user interface, enhancing user experience by not requiring users to navigate away from Salesforce to view the data.
* Implement this by using Tableau's embedding code, which can be generated from the Tableau Server for each specific view. Place this embed code into the Salesforce Visualforce pages or use Salesforce Canvas to integrate these views seamlessly.
* This setup allows the Tableau views to inherit user credentials from Salesforce, enabling personalized data visualization based on the user's access rights and region, directly aligned with their Salesforce login session.
ReferencesThe embedding technique is documented in both Tableau's and Salesforce's official integration guides, which provide step-by-step instructions on embedding Tableau views into Salesforce platforms.
質問 # 38
A client has a large data set that contains more than 10 million rows.
A consultant wants to calculate a profitability threshold as efficiently as possible. The calculation must classify the profits by using the following specifications:
. Classify profit margins above 50% as Highly Profitable.
. Classify profit margins between 0% and 50% as Profitable.
. Classify profit margins below 0% as Unprofitable.
Which calculation meets these requirements?
- A. IF [ProfitMargin]>0.50 Then 'Highly Profitable'
ELSEIF [ProfitMargin]>=0 Then 'Profitable'
ELSE 'Unprofitable'
END - B. IF [ProfitMargin]>0.50 Then 'Highly Profitable'
ELSEIF [ProfitMargin]>=0 Then 'Profitable'
ELSEIF [ProfitMargin] <0 Then 'Unprofitable'
END - C. IF [ProfitMargin]>=0.50 Then 'Highly Profitable'
ELSEIF [ProfitMargin]>=0 Then 'Profitable'
ELSE 'Unprofitable'
END - D. IF([ProfitMargin]>=0.50,'Highly Profitable', 'Profitable')
ELSE 'Unprofitable'
END
正解：C
解説：
The correct calculation for classifying profit margins into categories based on specified thresholds involves the use of conditional statements that check ranges in a logical order:
* Highly Profitable Classification: The first condition checks if the profit margin is 50% or more. This must use the ">=" operator to include exactly 50% as "Highly Profitable".
* Profitable Classification: The next condition checks if the profit margin is between 0% and 50%. Since any value falling at or above 50% is already classified, this condition only needs to check for values greater than or equal to 0%.
* Unprofitable Classification: The final condition captures any remaining scenarios, which would only be values less than 0%.
References:
* Logical Order in Conditional Statements: It is crucial in programming and data calculation to ensure that conditions in IF statements are structured in a logical and non-overlapping manner to accurately categorize all possible values.
質問 # 39
A client wants to flag orders that have sales higher than the regional average.
Which calculated field will produce the required result?
- A. { FIXED [Order ID] : SUM([Sales]) }
>
{ FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) } - B. { FIXED [Order ID] : SUM([Sales]) }
>
{ FIXED [Region] : SUM([Sales]) } - C. [Sales]
>
{ FIXED [Order ID] : SUM([Sales]) } - D. { FIXED [Order ID] : SUM([Sales]) }
>
{ INCLUDE [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) }
正解：A
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 # 40
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