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Salesforce Analytics-Con-301 Exam Syllabus Topics:
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NEW QUESTION # 19
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. Creator
- B. Site Administrator
- C. Viewer
- D. Explorer (can publish)
Answer: D
Explanation:
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.
NEW QUESTION # 20
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. Update the Widget Inventory Table to be a daily snapshot.
- B. Use Tableau Desktop to visualize null values.
- C. Use Tableau Prep to add new rows.
- D. Create a temporary table in the database.
Answer: C
Explanation:
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.
NEW QUESTION # 21
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. POWER(ZN(SUM([Sales]))/
LOOKUP(ZN(SUM([Sales])), FIRST()),ZN(1/(INDEX()-1)))
- 1 - B. CASE [Sector Parameter]
WHEN 1 THEN "green"
WHEN 2 THEN "yellow" - C. ZN([Sales])*(1 - ZN([Discount]))
- D. SUM([Profit])/SUM([Sales])
Answer: D
Explanation:
END
Explanation:
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.
NEW QUESTION # 22
A Tableau Cloud client has requested a custom dashboard to help track which data sources are used most frequently in dashboards across their site.
Which two actions should the client use to access the necessary metadata? Choose two.
- A. Connect directly to the Site Content data source within the Admin Insights project.
- B. Download metadata through Tableau Catalog.
- C. Query metadata through the GraphiQL engine.
- D. Access metadata through the Metadata API.
Answer: C,D
Explanation:
To track which data sources are used most frequently across a site in Tableau Cloud, the client should use the GraphiQL engine and the Metadata API. The GraphiQL engine allows for interactive exploration of the metadata, making it easier to construct and test queries1. The Metadata API provides access to metadata and lineage of external assets used by the content published to Tableau Cloud, which is essential for tracking data source usage2.
References: The actions are based on the capabilities of the GraphiQL engine and the Metadata API as described in Tableau's official documentation and learning resources321.
NEW QUESTION # 23
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 the Relative Date filter with a Multiple Values (Dropdown) filter on YEAR([Created Date]).
- B. Replace the TOTAL([Opportunities]) calculation with a Grand Total.
- C. Replace SUM([Value]) with WINDOW_SUM([Value]).
- D. Replace the Multiple Values (Dropdown) filter with a Multiple Values (Custom List) filter.
Answer: A
Explanation:
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.
References
This 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.
NEW QUESTION # 24
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 ('day', DATEADD('week', 4, [Order Date]))
Calc2: AVG([Order Date] - [Calc1]) - B. Calc1: DATETRUNC ('month', DATEADD('month', 1, [Order Date]))
Calc2: AVG(DATEDIFF ('week', [Order Date], [Calc1])) - C. Calc1: DATETRUNC ('day', DATEADD ('day', 31, [Order Date]))
Calc2: AVG ([Order Date] - [Calc1]) - D. Calc1: DATETRUNC ('month', DATEADD ('month', 1, [Order Date]))
Calc2: AVG(DATEDIFF ('day', [Order Date], [Calc1]))
Answer: D
Explanation:
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 using DATETRUNC('month', DATEADD('month', 1, [Order Date])). Then, the average difference in days between the order date and the shipping date is calculated using AVG(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.
NEW QUESTION # 25
A client calculates the percent of total sales for a particular region compared to all regions.
Which calculation will fix the automatic recalculation on the % of total field?
- A. {FIXED [Region]:sum([Sales])}
- B. {FIXED [Region]:[Sales]}/{FIXED: SUM([Sales])}
- C. {FIXED [Region]:sum([Sales])}/{FIXED :SUM([Sales])
- D. {FIXED [Region]:sum([Sales])}/SUM([Sales]}
Answer: D
Explanation:
To correctly calculate the percent of total sales for a particular region compared to all regions, and to ensure that the calculation does not get inadvertently recalculated with each region filter application, the recommended calculation is:
{FIXED [Region]: sum([Sales])}: This part of the formula computes the sum of sales for each region, regardless of any filters applied to the view. It uses a Level of Detail expression to fix the sum of sales to each region, ensuring that filtering by regions won't affect the calculated value.
SUM([Sales]): This part computes the total sum of sales across all regions and is recalculated dynamically based on the filters applied to other parts of the dashboard or worksheet.
Combining the two parts: By dividing the fixed regional sales by the total sales, we get the proportion of sales for each region as compared to the total. This calculation ensures that while the denominator adjusts according to filters, the numerator remains fixed for each region, accurately reflecting the sales percentage without being affected by the region filter directly.
References
This calculation follows Tableau's best practices for using Level of Detail expressions to manage computation granularity in the presence of dashboard filters, as outlined in the Tableau User Guide and official Tableau training materials.
NEW QUESTION # 26
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. Radar Chart
- B. Chord Chart
- C. Sankey Diagram
- D. Tree Chart
Answer: C
Explanation:
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.
NEW QUESTION # 27
A client wants to report Saturday and Sunday regardless of the workbook's data source's locale settings.
Which calculation should the consultant recommend?
- A. DATEPART('iso-weekday', [Order Date])>=6
- B. DATENAME('iso-weekday', [Order Date])>=6
- C. DATEPART('iso-weekday', [Order Date])=1 or DATEPART('iso-weekday', [Order Date])=7
- D. DATEPART('weekday', [Order Date])>=6
Answer: C
Explanation:
The calculation DATEPART('iso-weekday', [Order Date])=1 or DATEPART('iso-weekday', [Order Date])=7 is recommended because the ISO standard considers Monday as the first day of the week (1) and Sunday as the last day (7). This calculation will correctly identify Saturdays and Sundays regardless of the locale settings of the workbook's data source, ensuring that the report includes these days as specified by the client.
References: The use of the 'iso-weekday' part in the DATEPART function is consistent with the ISO 8601 standard, which is independent of locale settings. This approach is supported by Tableau's documentation on date functions and their behavior with different locale settings123.
To accurately identify weekends across different locale settings, using the 'iso-weekday' component is reliable as it is consistent across various locales:
ISO Weekday Function: The ISO standard treats Monday as the first day of the week (1), which makes Sunday the seventh day (7). This standardization helps avoid discrepancies in weekday calculations that might arise due to locale-specific settings.
Identifying Weekends: The calculation checks if the 'iso-weekday' part of the date is either 1 (Sunday) or 7 (Saturday), thereby correctly identifying weekends regardless of the locale settings.
References:
Handling Locale-Specific Settings: Using ISO standards in date functions allows for uniform results across systems with differing locale settings, essential for consistent reporting in global applications.
NEW QUESTION # 28
A client wants to count all the distinct orders placed in 2010. They have written the following calculation, but the result is incorrect.
IF YEAR([Date])=2010 THEN COUNTD ([OrderID]) END
Which calculation will produce the correct result?
- A. IF MIN(YEAR([Date]))=2010 THEN WINDOW_COUNTD([OrderID]) END
- B. COUNTD(IF YEAR([Date])=2010 THEN [OrderID] END)
- C. COUNT(IF YEAR([Date])=2010 THEN [OrderID] END)
- D. IF YEAR([Date])=2010 THEN {COUNTD ([OrderID])} END
Answer: B
Explanation:
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 the COUNTD function within an IF statement, which does not work as expected because the COUNTD function cannot conditionally count within the scope of the IF statement.
Correct Calculation: COUNTD(IF YEAR([Date]) = 2010 THEN [OrderID] END). This calculation checks each order date; if the year is 2010, it returns the OrderID. The COUNTD function then counts all unique OrderIDs 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.
References
This 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".
NEW QUESTION # 29
SIMULATION
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.
Answer:
Explanation:
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.
NEW QUESTION # 30
A client wants to see data for only the last day in a dataset and the last day is always yesterday. The date is represented with the field Ship Date.
The client is not concerned about the daily refresh results. The volume of data is so large that performance is their priority. In the future, the client will be able to move the calculation to the underlying database, but not at this time.
The solution should offer the best performance.
Which approach should the consultant use to produce the desired results?
- A. Filter on 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.
Answer: A
Explanation:
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 of TODAY()-1 ensures 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.
References
This 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.
NEW QUESTION # 31
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 in each view of each report using set filters and option Server/Create User Filter.
- B. Create User filters based on data policies and apply them to a published data source.
- C. 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.
- D. Create User filters based on data policies and apply them to views using set filters and option Server/Create User Filter.
Answer: B
Explanation:
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.
NEW QUESTION # 32
A client has a published dashboard. They change the dashboard and then republish it. Now, users report that their web browser bookmarks to the dashboard are broken.
What are two possible causes for this issue? Choose two.
- A. Tableau Server was upgraded.
- B. The dashboard was published to a different project.
- C. The dashboard was published with a new name.
- D. New credentials were embedded into the data source.
Answer: B,C
Explanation:
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.
NEW QUESTION # 33
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 for each type of device and fix the size of the layouts.
- B. Build one dashboard and fix the size of the dashboard.
- C. Build one dashboard and set the size to Automatic.
- D. Build one dashboard that has desktop, tablet, and phone layouts, and fix the size of the layouts.
Answer: D
Explanation:
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.
NEW QUESTION # 34
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 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.
- 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 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.
Answer: B
Explanation:
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.
NEW QUESTION # 35
A multi-national company wants to have a Tableau dashboard that will provide country-level information for both its forecast summaries and year-on-year metrics. The company wants to toggle between these two views while leaving main key performance indicators (KPIs) visible on the main dashboard.
Which method is the most efficient in achieving the company's requirements?
- A. Create a dashboard with the sheets containing the main KPIs and the forecast summary worksheet.
. Duplicate this dashboard and replace the forecast view worksheet with the year-on-year metrics worksheet.
. Add navigation buttons to both dashboards. - B. Create a Boolean parameter with the two names of the views as aliases and a corresponding calculated field with the following calculation: True.
. Add the forecast summary sheet to the dashboard and add the year-on-year metrics sheet to the same dashboard as a Floating dashboard object. - C. Create a parameter that accepts values from a list that contains "Forecast View" and "Year-on-Year View."
. Right-click the parameter and select Add to Sheet for both worksheets.
. Navigate back to the dashboard and to the upper corner of the two worksheets.
. Enable the Use as Filter option. - D. Create a single worksheet with all the measures required for both the forecast summary and the year-on-year views.
. Create a Boolean parameter and a corresponding calculated field with the following calculation: True.
. Add a blank dashboard object and in the Layout tab, check the box for "Control visibility using value" and select the parameter you created.
Answer: B
Explanation:
. Add the calculated fields as a Detail under the Marks card of the floating view, create a "Change Parameter" action, and set the
"Target Parameter" and "Source Fields" to the parameter and calculated field you created.
. Check the box for "Control visibility using value" in the Layout tab of the floating view and select the parameter you created.
Explanation:
The most efficient method for toggling between two views (forecast summaries and year-on-year metrics) while keeping main KPIs visible involves using a parameter and calculated fields for controlling visibility:
Create a Boolean Parameter: This parameter will have two aliases representing the two views ("Forecast View" and "Year-on-Year View"). This allows the user to select which view they wish to see directly from the dashboard.
Calculated Field: Create a calculated field that always returns True. This field acts as a constant placeholder to enable the visibility control tied to the parameter.
Dashboard Setup: Place both the forecast summary and the year-on-year metrics sheets on the dashboard. Set the year-on-year metrics sheet as a floating object over the forecast summary.
Visibility Control: Use the "Control visibility using value" option in the Layout tab for the floating year-on-year metrics view. Tie this setting to the Boolean parameter so that changing the parameter will show or hide this view without affecting the main KPIs displayed on the dashboard.
Interactivity: Implement a "Change Parameter" dashboard action where selecting different options in the dashboard (e.g., clicking on certain parts) triggers the parameter to change, thus toggling the visible view.
References
This method leverages Tableau's dashboard interactivity features including parameters, calculated fields, and visibility settings, as recommended in Tableau's user guide on dynamic dashboard design.
NEW QUESTION # 36
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. {FIXED [Customer ID], [Region]: COUNTD([Order ID])}
- C. {EXCLUDE [Customer ID]: COUNTD([Order ID])}
- D. {FIXED [Customer ID], [Region], [Order Date]: COUNTD([Order ID])}
Answer: B
Explanation:
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.
NEW QUESTION # 37
SIMULATION
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.
Answer:
Explanation:
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.
NEW QUESTION # 38
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 the table calculation used to create labels.
- B. Filter with a table calculation WINDOW_AVG(MIN([Profit]), first(), last())
- C. Add the [Profit] filter to the context.
- D. Use a calculation with TOTAL() function instead of a quick table calculation.
Answer: C
Explanation:
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.
NEW QUESTION # 39
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 a live connection to Tableau Server.
- B. Publish an incremental extract refresh every day and perform a full extract refresh every Saturday.
- C. Publish an incremental extract refresh every day and publish a secondary data set containing data revisions.
- D. Publish an incremental refresh every Saturday.
Answer: B
Explanation:
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.
NEW QUESTION # 40
SIMULATION
Refer to the exhibit.
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\.
Answer:
Explanation:
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.
NEW QUESTION # 41
A client creates a report and publishes it to Tableau Server where each department has its own user group set on the server. The client wants to limit visibility of the report to the sales and marketing groups in the most efficient manner.
Which approach should the consultant recommend?
- A. Use user groups defined on Tableau Server to build user filters in the report's data source.
- B. Add user filters from Tableau Server to each worksheet and select only sales and marketing user groups.
- C. Grant access to the report on the Tableau Server only to the members of sales and marketing user groups.
- D. Prepare a row-level security (RLS) entitlement table to define limitations of the access and use it to build user filters in the report's data source.
Answer: C
Explanation:
The most efficient way to limit report visibility to specific user groups on Tableau Server is to manage permissions directly on the server. By granting access to the report only to the sales and marketing user groups, the client ensures that only members of these groups can view the report. This method is straightforward and does not require the additional steps involved in setting up row-level security or user filters.
References: The approach is supported by best practices in managing user permissions and visibility on Tableau Server, as described in the Tableau Community and official Tableau resources12.
NEW QUESTION # 42
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