Analytics-Con-301試験問題集を提供していますSalesforce問題
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Salesforce Analytics-Con-301 認定試験の出題範囲：
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| トピック 2 |
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質問 # 61
A client has a data source that stores a time stamp for each time a user interacts with a product feature. They visualize 3 years of data at the daily level. As adoption has grown over the last 6 months, the dashboard performance has steadily decreased, despite connecting via a data extract that is set to refresh every hour.
A Tableau consultant needs to improve performance of the dashboard with the least impact to the visualization.
Which option meets these requirements without additional cost?
- A. Decrease the extract refresh to once a day.
- B. Invest in an extract, transform, load (ETL) tool to aggregate the data to a daily level.
- C. Leverage Tableau Prep to aggregate the data to the daily product level.
- D. Add extract filters to limit the number of product features visualized.
正解：C
解説：
Comprehensive and Detailed Explanation From Exact Extract:
The dataset contains timestamps for each individual user interaction. Growth in user adoption over 6 months means the number of rows has expanded significantly. Tableau's performance documentation states that large row-level datasets can cause performance degradation even when using extracts, especially when:
* The visualization is aggregated to a higher level (such as daily), and
* The underlying extract still contains much more granular data than needed.
Tableau recommends pre-aggregating data before it reaches Tableau Desktop, which reduces extract size, memory use, and query time. This improves performance without changing what the visualization displays.
Option D uses Tableau Prep, which is included with Tableau Creator licensing and therefore incurs no additional cost. Tableau Prep can aggregate raw timestamp data into daily totals per product feature, which matches the visualization's actual granularity. This results in:
* A dramatically smaller extract
* Faster queries
* No change to how the dashboard looks or functions
Option A would remove product features from the visualization, altering the dashboard content and reducing insight, which does not meet the requirement of minimal impact.
Option B requires purchasing an external ETL tool, which violates the requirement of no additional cost.
Option C reduces the number of extract refreshes but does not improve dashboard performance; the data would remain equally granular and equally slow.
Therefore, Tableau Prep aggregation is the correct solution that improves performance while maintaining the same visualization and incurring no additional cost.
* Tableau performance guidelines recommending pre-aggregation of highly granular datasets.
* Tableau Prep documentation stating it can be used to aggregate data before creation of extracts.
* Tableau's extract optimization guidance describing how reducing row counts improves query and visualization performance.
質問 # 62
A worksheet uses a LOOKUP function to display Sales by Month, Year of Order Date, and sales from the last
12 months. A consultant wants to use a Relative Date Filter to filter for data from the last 12 months.
However, when the consultant does this, the prior year's data is removed from the sheet.
Which two actions should the consultant take to retain the prior year's data after applying the filter? Choose two.
- A. Replace the LOOKUP function with a FIXED Level of Detail (LOD) expression.
- B. Create the following calculation: DATEDIFF('month', [Order Date], {MAX([Order Date])}) < 12. Hide all False values.
- C. Set the Relative Date filter as a Context Filter instead of Measure Filter.
- D. Create the following calculation: LOOKUP(MIN([Order Date]),0). Filter on that calculation instead of Order Date.
正解：C、D
解説：
Comprehensive and Detailed Explanation From Exact Extract:
A LOOKUP table calculation requires access to rows outside the filtered date range so that the calculation can reference prior data. When a Relative Date Filter removes older data before the table calculation is evaluated, the LOOKUP loses the needed rows, causing the prior year's data to disappear.
Tableau's order of operations states:
* Relative Date Filters act early (at the dimension filter stage).
* Table calculations act very late.
* To preserve table calculation context, filters must not remove necessary rows.
Two Tableau-documented approaches address this:
Explanation for B
Setting the Relative Date Filter as a Context Filter allows table calculations to operate on the full dataset needed for LOOKUP. Context filters create a separate temporary table, and subsequent filters like table calculations evaluate after the context is established.
This ensures older rows are still available to the LOOKUP function.
Explanation for C
Creating a field such as:
LOOKUP(MIN([Order Date]), 0)
and filtering on this field instead of Order Date converts the filter into a table calculation filter, which occurs after the LOOKUP computation. Tableau documentation explains that table calculation filters preserve the full dataset required for the LOOKUP window.
This ensures that the LOOKUP still has access to last year's values even when filtering for the current 12 months.
Why A is incorrect
Replacing LOOKUP with an LOD changes the logic entirely.
LOD expressions cannot replicate moving-window or lag-type behavior.
Why D is incorrect
DATEDIFF logic can replicate a rolling window, but hiding False values is essentially a manual filter and does not preserve the integrity of the LOOKUP's required partitioning. It also contradicts Tableau's recommended approach for maintaining table calculation context.
* Tableau Order of Operations explaining why table calculation filters preserve data for LOOKUP.
* Tableau documentation on context filters and how they allow more data to remain available for downstream table calculations.
* Tableau guidance on how Relative Date Filters interact with table calculations.
* Best practices for preserving table calculation window rows when filtering.
質問 # 63
A university has data on its undergraduate students and their majors by grade level (Freshman, Sophomore, Junior, Senior). The university is interested in visualizing the path students take as they change majors across grade levels.
Which visualization type should the consultant recommend?
- A. Sankey Diagram
- B. Radar Chart
- C. Chord Chart
- D. Tree Chart
正解：A
解説：
To visualize the path students take as they change majors across different grade levels, a Sankey Diagram is highly effective. This type of visualization illustrates the flow and quantity between different stages or categories:
Sankey Diagram: It allows for a visual representation of students' movements between majors over time. Each flow's thickness is proportional to the number of students moving from one major to another, giving a clear, immediate visual cue of major popularity and student migration patterns.
To create a Sankey Diagram in Tableau, you typically need to prepare the data specifically for this type of chart. The data must include source (starting major), target (ending major), and the value (number of students). It often requires custom calculations and data reshaping to get the data in a format that a Sankey can use.
Once the data is prepared, you can use a combination of calculated fields, path binning, and line charts to simulate the flow effect in Tableau. External plugins or web-based integrations might also be employed for more direct implementations.
References
Sankey Diagrams are not natively supported in Tableau but can be implemented through creative use of data preparation and calculations, as suggested in advanced Tableau user communities and demonstrated in various Tableau public galleries.
質問 # 64
A company's Tableau Cloud admin wants to maintain control over what content gets published to its site for viewers, while also supporting self-service for dashboard creators.
Which governance strategy should the admin implement?
- A. Create sandbox projects to contain ad hoc content and production projects for validated content.
- B. Restrict users' permission to view data sources used in uncertified dashboards.
- C. Allow dashboard creators to publish to their Personal Space and for site administrators to move content to projects.
- D. Maintain a separate sandbox site and use the Content Migration Tool to promote content between sites.
正解：A
解説：
Comprehensive and Detailed Explanation From Exact Extract:
Tableau's recommended content governance model for Server and Cloud emphasizes project-based separation between development ("sandbox") content and certified, production-ready content.
Key points from Tableau governance guidance:
* Organizations should define sandbox projects where creators can freely publish and iterate on workbooks and data sources.
* Once content is reviewed and validated, it is promoted into "production" projects that are designated for trusted content for viewers.
* This model allows self-service authoring while keeping tight control over what is exposed to broad viewer audiences.
Option A exactly reflects this model: sandbox projects for ad hoc content, and production projects for validated content.
Option B uses separate sites and the Content Migration Tool, which is heavier to manage and usually reserved for cross-environment moves (such as dev to prod), not necessary for basic project-level governance in a single Tableau Cloud site.
Option C relies on Personal Space. Tableau recommends Personal Space for private drafts, not as the main promotion path, and it is not the primary governance pattern for viewer-facing content.
Option D restricts data source viewing but does not provide a full governance strategy for managing ad hoc versus production dashboards.
Therefore, the correct strategy is sandbox projects plus production projects, which is option A.
* Tableau governance whitepapers describing sandbox versus production projects as a best-practice pattern.
* Tableau Cloud site administration guidance recommending project structure for self-service and controlled promotion of content.
質問 # 65
A customer migrated from Tableau Server to Tableau Cloud. However, there is still private network data behind the corporate firewall that Tableau Cloud needs to access securely.
Which data connection strategy should a Tableau consultant advise with minimal software maintenance by the customer?
- A. Direct Connect
- B. Data Connect
- C. Tableau Bridge
- D. Private Connect
正解：B
解説：
Comprehensive and Detailed Explanation From Exact Extract:
The question focuses on two key requirements:
* Tableau Cloud must access private network data behind a corporate firewall.
* The customer wants minimal software maintenance.
To determine the correct answer, each option must be evaluated based on Tableau's official documented behavior.
Why Option A (Tableau Bridge) Is Not the Best Answer
Tableau Bridge is described in Tableau documentation as a self-managed solution used to connect Tableau Cloud to private network/on-premises data.
Key characteristics of Bridge include:
* It is installed and maintained by the customer on a machine behind the firewall.
* It requires ongoing updates, monitoring, and administration by the customer.
Because the question specifically asks for minimal software maintenance, Bridge does not meet the requirement.
Why Option B (Private Connect) Is Not the Best Answer
Private Connect is a feature allowing Tableau Cloud to connect privately and securely to AWS-hosted cloud data sources using private networking.
However:
* It is primarily intended for AWS-based data services (such as Snowflake on AWS, Amazon Redshift, Athena).
* The question describes private network data behind a corporate firewall, which usually refers to on- premises data, not cloud-hosted AWS services.
* Therefore, Private Connect is not the generally applicable solution for the scenario described.
Why Option C (Direct Connect) Is Incorrect
"Direct Connect" is not an official Tableau Cloud feature for connecting to private network data.
This option can be eliminated immediately.
Why Option D (Data Connect) Is the Correct Answer
Tableau's Data Connect service is documented as:
* A solution that provides secure access to private network or on-premises data, similar in purpose to Bridge.
* A remotely managed, monitored, and streamlined solution where Tableau manages the underlying Kubernetes cluster.
* A service that reduces administrative overhead for the customer by allowing Tableau to handle cluster management, monitoring, and maintenance.
Tableau documentation clearly states:
* Data Connect provides access to private network data similar to Bridge.
* But unlike Bridge, it is designed to reduce the overhead of administration because Tableau remotely manages and maintains the cluster used to provide connectivity.
* It follows a shared responsibility model where the customer provides compute resources, and Tableau manages the software layer-including maintenance and monitoring.
This directly satisfies the scenario's requirement:
"Minimal software maintenance by the customer."
Thus, among the options provided, Data Connect is the correct and most appropriate answer.
References From Tableau Consultant / Study Materials
* Tableau documentation describing Tableau Bridge as a self-managed proxy client installed behind the firewall.
* Tableau documentation describing Data Connect as a remotely managed, monitored, and streamlined solution for accessing private network data.
* Tableau documentation explaining the shared responsibility model for Data Connect, where Tableau handles cluster management and reduces the customer's administrative overhead.
* Tableau materials comparing Bridge vs. Data Connect, stating that Data Connect reduces administration and enables more scalable private network connectivity.
* Tableau information noting that Private Connect is designed for AWS-hosted cloud data, not general private network on-premises data.
質問 # 66
A Tableau consultant is asked to evaluate a workbook that is slow to respond and make a recommendation on possible performance improvements. The workbook connects to three extract data sources from an SQL database. The sheets are used in five dashboards. The consultant runs a performance recording on the workbook and notices that the largest amount of time is spent on rendering the visualizations.
What is the most effective approach to reduce the workbook's rendering time?
- A. Change the dashboards' size to Automatic.
- B. Change the connections to live.
- C. Update worksheets to reduce the number of records displayed.
- D. Filter the unused data before bringing it into the workbook.
正解：C
解説：
Comprehensive and Detailed Explanation From Exact Extract:
According to Tableau's Performance Optimization guidance, rendering time becomes the largest bottleneck when excessive marks, dense data, or overly complex visualizations appear on worksheets. Rendering is the last stage in the Tableau Order of Operations and is directly affected by how many marks must be drawn and how visually complex each view is.
Tableau's performance recommendations explain:
* When a performance recording shows that Rendering is the slowest step, the most effective improvement is to reduce the number of marks (records) in the view.
* Rendering time is determined by the number of marks, shapes, headers, labels, and visual elements Tableau must draw.
* Reducing the amount of data displayed on each worksheet is the most impactful change when rendering is the dominant delay.
Option B directly aligns with this: updating worksheets to reduce the number of records displayed lowers the number of marks, reduces visual density, and improves rendering speed.
Option A is not effective because changing dashboard size does not reduce the number of marks.
Option C would degrade performance because live connections are typically slower than extracts.
Option D improves data preparation and may reduce extract load times, but it does not directly address rendering unless the unused data was contributing to marks in the view. The question indicates the bottleneck is specifically rendering, so reducing marks is the most appropriate action.
Therefore, the most effective solution to reduce rendering time is to reduce the number of records (marks) displayed on worksheets.
* Tableau Performance Recording guidance describing rendering as the slowest stage when too many marks are present.
* Tableau Performance Checklist recommending reducing the number of marks in views to improve rendering.
* Tableau Desktop help sections on best practices for improving visualization performance when rendering dominates.
質問 # 67
A client collects information about a web browser customers use to access their website. They then visualize the breakdown of web traffic by browser version.
The data is stored in the format shown below in the related table, with a NULL BrowserID stored in the Site Visitor Table if an unknown browser version accesses their website.
The client uses "Some Records Match" for the Referential Integrity setting because a match is not guaranteed. The client wants to improve the performance of the dashboard while also getting an accurate count of site visitors.
Which modifications to the data tables and join should the consultant recommend?
- A. Continue to use NULL as the BrowserID in the Site Visitor Table and change the Referential Integrity to "All Records Match."
- B. Continue to use NULL as the BrowserID in the Site Visitor Table and leave the Referential Integrity set to "Some Records Match."
- C. Add an "Unknown" option to the Browser Table, reference its BrowserID in the Site Visitor Table, and leave the Referential Integrity set to
"Some Records Match." - D. 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."
正解：D
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 # 68
A consultant is designing a dashboard that will be consumed on desktops, tablets, and phones. The consultant needs to implement a dashboard design that provides the best user experience across all the platforms.
Which approach should the consultant take to achieve these results?
- A. Build one dashboard and set the size to Automatic.
- B. Build one dashboard that has desktop, tablet, and phone layouts, and fix the size of the layouts.
- C. Build one dashboard and fix the size of the dashboard.
- D. Build one dashboard for each type of device and fix the size of the layouts.
正解：B
解説：
For a consultant designing a dashboard to be consumed across multiple device types, the best approach is:
* Multi-device Layout: Tableau provides the capability to design device-specific layouts within a single dashboard. This feature allows the dashboard to adapt its layout to best fit the screen size and orientation of desktops, tablets, and phones.
* Fixed Size Layouts: By fixing the size of each layout, the consultant can ensure that the dashboard appears consistent and maintains the intended design elements and user experience across devices.
Fixed sizes prevent components from resizing in ways that could disrupt the dashboard's readability or functionality.
* Implementation: In Tableau, you can create these layouts by selecting 'Device Preview' and adding custom layouts for each device type. Here, you define the dimensions and the positioning of sheets and controls tailored to each device's typical viewing mode.
References
This approach leverages Tableau's device designer capabilities, which are specifically designed to optimize dashboards for multiple viewing environments, ensuring a seamless user experience regardless of the device used. This functionality is well documented in Tableau's official guides on creating and managing device- specific dashboards.
質問 # 69
A consultant is working with a Tableau Server customer. The customer asks the consultant if there is a need to upgrade their instance of Tableau Server that was installed over 1 year ago.
Which two situations justify the need for an upgrade? Choose two.
- A. Compatibility with newer Tableau Desktop and Prep Builder versions that users might want to install
- B. The security team has a report of bugs and security vulnerabilities in older versions of Tableau
- C. The need to access new features and improvements from Tableau Cloud
- D. Users are complaining of performance; the upgrade will reduce the server's hardware requirements
正解：A、B
解説：
Comprehensive and Detailed Explanation From Exact Extract:
Tableau's upgrade guidance identifies two major drivers for upgrading Tableau Server:
* Version Compatibility with Tableau Desktop and Tableau Prep Builder
* Tableau Server must be equal to or newer than the version used by Desktop and Prep Builder for publishing.
* Organizations upgrading Desktop often must upgrade Server to avoid compatibility issues.
* This is a core reason to update a year-old installation.
* Security and Bug Fixes
* Tableau regularly publishes security patches, bug fixes, and stability enhancements.
* Older versions accumulate unresolved security issues that may be identified by security teams.
* Tableau explicitly states that upgrading ensures the instance receives the latest security protections.
Option B is incorrect because upgrading does not reduce hardware requirements; in many cases hardware needs may increase.
Option C is incorrect because Tableau Cloud features do not require upgrading Tableau Server. Tableau Cloud enhancements are independent of Server versions.
Therefore, the two conditions that justify upgrading are maintaining compatibility and addressing security vulnerabilities.
* Tableau's version compatibility matrix requiring alignment between Tableau Server and Desktop/Prep.
* Upgrade planning documents emphasizing security patches and bug fixes as key upgrade drivers.
* Notes describing that performance complaints alone are not solved simply by upgrading.
質問 # 70
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 # 71
A client wants guidance for Creators to build efficient extracts from large data sources.
What are three Tableau best practices that the Creators should use? Choose three.
- A. Keep only the data required for analysis by using extract filters.
- B. Include all the data from the original data source in the extract.
- C. Use aggregate data for visible dimensions, whenever possible.
- D. Hide all unused fields.
- E. Use only live connections as they are always faster than extracts.
正解：A、C、D
解説：
To build efficient extracts from large data sources, it is crucial to minimize the load and optimize the performance of the extracts:
A . Keep only the data required for analysis by using extract filters: This best practice involves using filters to reduce the volume of data extracted, thus focusing only on the data necessary for analysis.
B . Use aggregate data for visible dimensions, whenever possible: Aggregating data at the time of extraction reduces the granularity of the data, which can significantly improve performance and reduce the size of the extract.
E . Hide all unused fields: Removing fields that are not needed for analysis from the extract reduces the complexity and size of the data model, which in turn enhances performance and speeds up load times.
These practices are endorsed in Tableau's official documentation and training sessions as effective ways to enhance the performance of Tableau extracts and optimize dashboard responsiveness.
質問 # 72
A consultant wants to improve the performance of reports by moving calculations to the data layer and materializing them in the extract.
Which type of calculation is the consultant able to move?
- A. A row-level calculation
- B. A calculation that contains parameters
- C. A calculation that contains table calculation functions
- D. A calculation that contains an aggregation
正解：A
解説：
Comprehensive and Detailed Explanation From Exact Extract:
Tableau allows certain calculations to be materialized in extracts, meaning they are precomputed and stored inside the .hyper file to improve performance.
According to Tableau's extract documentation:
* Materializable calculations must be compatible with the extract engine and must not depend on dynamic, view-based, or post-query logic.
* Only row-level calculations and aggregation-level calculations without dependencies on runtime context can be materialized.
* Tableau cannot materialize any calculation containing:
* Table calculation functions
* Functions requiring post-aggregation logic
* View-dependent elements
* Parameters that need runtime evaluation
Evaluation of the choices:
A). A row-level calculation - Correct
Row-level calculations operate on each record individually before aggregation.
Tableau documentation specifies that these calculations can be pushed down into the extract and materialized because they do not depend on the visualization or user interaction.
Examples include concatenation, arithmetic, string manipulation, and row-based logic such as:
[Sales] * [Quantity] or IF [Region] = 'West' THEN 1 END
These can be precomputed inside the extract, improving performance.
B). A calculation that contains table calculation functions - Not allowed Table calculations (WINDOW_SUM, INDEX, RUNNING_SUM, RANK, etc.) depend on the table structure after aggregation and query execution.
Therefore, Tableau documentation states they cannot be materialized in extracts.
C). A calculation that contains parameters - Not allowed
Parameters are evaluated at runtime, meaning the user can change their value.
Because of this, Tableau cannot permanently compute and store such a calculation inside an extract.
D). A calculation that contains an aggregation - Generally not materialized Aggregated calculations often depend on query context and cannot always be materialized.
Only simple, context-free aggregations might be materialized, but Tableau explicitly warns that aggregations are not guaranteed candidates for extract materialization.
Thus, this is not the best answer compared to row-level logic.
Conclusion
Only row-level calculations meet Tableau's exact requirements for materialization in extracts.
References From Tableau Consultant Documentation
* Tableau Extract documentation describing materializable calculation types.
* Tableau guidance stating table calculations and parameter-dependent calculations cannot be materialized.
* Extract optimization guidelines describing row-level logic as eligible for materialization.
質問 # 73
From the desktop, open the CC workbook.
Open the City Pareto worksheet.
You need to complete the Pareto chart to show the percentage of sales compared to the percentage of cities.
The chart must show references lines to visualize how the data compares to the Pareto principle.
From the File menu in Tableau Desktop, click Save.
正解：
解説：
See the complete Steps below in Explanation:
Explanation:
To complete the Pareto chart in the "City Pareto" worksheet of your Tableau Desktop and add reference lines to illustrate how the data compares to the Pareto principle, 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 City Pareto worksheet by selecting its tab at the bottom of the window.
* Construct the Pareto Chart:
* Ensure that sales data is aggregated by city. If not, drag the 'City' dimension to the Columns shelf and the 'Sales' measure to the Rows shelf.
* Sort the sales data in descending order to properly align the cities according to their sales contribution.
* To create a running total of sales, right-click on the 'Sales' measure on the Rows shelf, select
'Quick Table Calculation', and choose 'Running Total'.
* Drag the 'Number of Records' field to the Rows shelf next to the Sales running total. Right-click on it, select 'Quick Table Calculation', and choose 'Running Total'. Set its calculation to 'Percent of Total' from the 'Edit Table Calculation' option to represent the percentage of cities.
* Add Reference Lines for the Pareto Principle:
* Click on the Analytics tab in the sidebar.
* Drag a 'Reference Line' element and drop it onto the chart area.
* Set the Reference Line for the Sales axis at 80% to represent the typical Pareto cutoff where 80% of effects come from 20% of causes.
* Add another Reference Line on the axis representing the percentage of cities, set at 20%, to visually assess the Pareto principle.
* Adjust the Appearance of the Chart:
* Format the reference lines by right-clicking on them, selecting 'Edit', and choosing a distinct style or color to make them stand out.
* Ensure the chart is clear and labels are appropriately adjusted for easy understanding of the data visualization.
* Save Your Changes:
* From the File menu, click 'Save' to ensure all your changes are stored.
References:
Tableau Help: Offers detailed guidance on creating Pareto charts and adding reference lines.
Tableau Visualization Best Practices: Provides tips on effectively displaying cumulative data and principles such as Pareto.
By following these steps, you will have successfully enhanced the City Pareto worksheet to include a complete Pareto chart with reference lines that illustrate how the sales data compares to the Pareto principle, making it easier to analyze and communicate the distribution of sales across cities.
質問 # 74
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. Use Tableau Desktop to visualize null values.
- B. Use Tableau Prep to add new rows.
- C. Create a temporary table in the database.
- D. Update the Widget Inventory Table to be a daily snapshot.
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 # 75
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. Joining the tables in Tableau Desktop
- B. Joining the tables in Tableau Prep
- C. Blending the first two tables with the third
- D. Relating the tables in Tableau Desktop
正解：D
解説：
For a performant live connection in Tableau Cloud, especially when dealing with large datasets that preclude the use of extracts, relating the tables in Tableau Desktop is the recommended approach. This method allows for flexibility in how the data is queried and can improve performance by leveraging Tableau's relationships feature, which optimizes queries for the underlying database.
References: The best practices for live connections in Tableau Cloud suggest using relationships to manage complex data models efficiently1. Additionally, Tableau's documentation on connecting data sources recommends using relationships for better performance with live connections2.
質問 # 76
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 based on data policies and apply them to a published data source.
- B. 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.
- C. Create User filters in each view of each report using set filters and option Server/Create User Filter.
- D. Create User filters based on data policies and apply them to views using set filters and option Server
/Create User Filter.
正解：A
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 (like USERNAME() or ISMEMBEROF()). 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.
References
This 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 # 77
A company has a sales team that is segmented by territory. The team's manager wants to make sure each sales representative can see only data relevant to that representative's territory in the team Sales Dashboard.
The team is large and has high turnover, and the manager wants the mechanism for restricting data access to be as automated as possible. However, the team does not have a Tableau Data Management license.
What should the consultant recommend to meet the company's requirements?
- A. Create one group for each territory and assign sales representatives to the appropriate groups. Map each group to a territory in the Sales Dashboard. Publish this dashboard to the Sales Dashboard project and ensure all users have permissions to view the dashboard.
- B. Create separate workbooks for each territory. Publish each dashboard to the same Sales Dashboard project, and set permissions so each sales representative can see only the dashboards for their territories.
- C. Create a user filter in the Sales Dashboard workbook and map each sales representative to the territories they are responsible for. Publish this dashboard to the Sales Dashboard project and ensure all users have permissions to view the dashboard.
- D. Create a data source by joining the sales data table to an entitlements data table. Add a data source filter to restrict access and publish the data source. Connect the Sales Dashboard to this published data source.
正解：D
解説：
To ensure that each sales representative sees only data relevant to their territory, the best approach in the absence of a Tableau Data Management license involves using a joined data source with entitlements:
Data Source Configuration: Create a data source that joins the sales data table with an entitlements table. The entitlements table contains mappings of sales representatives to their respective territories.
Data Source Filter: Implement a data source filter that restricts data based on the current user's access rights. This filter references the joined entitlements to dynamically control data visibility based on the logged-in user.
Publishing the Data Source: Publish this filtered data source to Tableau Server. All workbooks or dashboards connecting to this data source inherently respect the row-level security established by the data source filter.
References
This approach aligns with Tableau's capabilities for implementing row-level security directly within the data source, as detailed in the Tableau security management and data modeling best practices.
質問 # 78
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. Ask Data
- C. Tableau Prep Conductor
- D. Tableau Resource Monitoring Tool
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 # 79
A client is migrating their data warehouse. They visualize the data in workbooks hosted on Tableau Server with Tableau Data Management enabled and want to see how many workbooks will be impacted.
What should the consultant do to quickly identify how many workbooks will be impacted?
- A. Open each workbook and identify the data source.
- B. In Tableau Server, select the database from External Assets, then select the Lineage tab.
- C. Complete the migration and let users report errors as they are noticed.
- D. Leverage the Tableau Developer API to query the workbooks' metadata.
正解：B
解説：
Comprehensive and Detailed Explanation From Exact Extract:
When Tableau Data Management is enabled, Tableau Catalog provides Lineage capabilities that map connections between:
* External databases
* Tables
* Data sources
* Workbooks
* Fields
Tableau documentation states that the Lineage tab for any external asset (such as a database or table):
* Shows all connected workbooks
* Shows dependencies and impact analysis
* Allows admins to instantly assess how many analytics assets will be affected by a data warehouse migration Option A directly uses Tableau Catalog to perform exactly this task.
Option B is unnecessary because the Catalog lineage tool already provides this information without development effort.
Option C is completely inappropriate because it offers no analysis or planning.
Option D is too time-consuming and unnecessary, especially when Tableau Catalog provides an automated dependency map.
Therefore, the correct method is to use the Lineage tab in External Assets.
* Tableau Catalog lineage documentation showing how to view impacted workbooks.
* External Assets and data source dependency features in Tableau Data Management.
* Impact analysis best practices for data warehouse migration using Tableau Catalog.
質問 # 80
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. {EXCLUDE [Customer ID]: COUNTD([Order ID])}
- D. {FIXED [Customer ID], [Region], [Order Date]: 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 # 81
A client is using the Tableau Content Migration Tool to move content from an old Tableau Server to a new Tableau Server.
Which content will need to be moved using a different tool or process?
- A. Published data sources that use live connections
- B. Tableau Prep flows
- C. Published data sources that use extracts
- D. Workbooks
正解：B
解説：
When migrating content between Tableau servers, certain types of content may require special consideration or different tools for migration:
Tableau Prep Flows: These are specific to Tableau Prep and are not included in the standard content migration capabilities of the Tableau Content Migration Tool. Tableau Prep flows often require separate processes for migration due to their distinct setup and integration with data sources and workflows.
Published Data Sources and Workbooks: These can typically be migrated directly using the Tableau Content Migration Tool, which supports moving published data sources (both live connections and extracts) and workbooks without requiring additional tools.
References:
Tableau Help and Support: Offers comprehensive tutorials and guidelines on using different tools for migrating various types of content, including the specific requirements for migrating Tableau Prep flows which are not covered by the standard content migration tool.
質問 # 82
A client wants to flag orders that have sales higher than the regional average.
Which calculated field will produce the required result?
- A. [Sales]
>
{ FIXED [Order ID] : SUM([Sales]) } - B. { FIXED [Order ID] : SUM([Sales]) }
>
{ INCLUDE [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) } - C. { FIXED [Order ID] : SUM([Sales]) }
>
{ FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) } - D. { FIXED [Order ID] : SUM([Sales]) }
>
{ FIXED [Region] : SUM([Sales]) }
正解：C
解説：
To flag orders with sales higher than the regional average, the correct calculated field would compare the sum of sales for each order against the average sales of all orders within the same region:
* Correct Formula: { FIXED [Order ID] : SUM([Sales]) } > { FIXED [Region] : AVG({ FIXED
[Order ID] : SUM([Sales]) }) }
* This calculation uses a Level of Detail (LOD) expression:
* The left part of the formula { FIXED [Order ID] : SUM([Sales]) } calculates the total sales for each individual order.
* The right part { FIXED [Region] : AVG({ FIXED [Order ID] : SUM([Sales]) }) } calculates the average sales per order within each region.
* The > operator is used to compare these two values to determine if the sales for each order exceed the regional average.
References
This 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 # 83
A consultant migrated a data source to improve performance. The consultant wants to identify which workbooks need to be updated to point to the new data source.
Which Tableau tool should the consultant use?
- A. Activity Log
- B. Tableau Advanced Management
- C. Data Management
- D. Prep Conductor
正解：B
解説：
To identify which workbooks need to be updated to point to a new data source after a migration, a consultant should use Tableau Advanced Management. This component of Tableau provides comprehensive management capabilities including the ability to track workbook dependencies and data source usage across your entire Tableau environment. Using Tableau Advanced Management allows consultants to assess the impact of changes in the data source on connected workbooks and efficiently manage updates.
質問 # 84
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