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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.

Comprehensive and Detailed Explanation From Exact Extract:

Tableau Cloud does not natively contain a Sankey diagram in Show Me. Such advanced charts often require:

A custom extension

Or specialized templates built into Tableau Exchange

A sandboxed Viz Extension allows users to embed specialized visualization components (like Sankey diagrams) securely in Tableau Cloud. These extensions are designed for advanced chart types that are not available natively.

Accelerators provide prebuilt dashboards but are not intended for custom visual types such as Sankey.

Connectors relate to connecting to data sources, not visualization.

Show Me does not include a Sankey option.

Therefore, downloading a sandboxed Viz Extension is the correct approach.

Viz Extensions documentation explaining support for custom charts, including Sankey.

Tableau Exchange listing providing sandboxed visualization extensions for non-native chart types.

Show Me panel documentation showing Sankey is not an included chart type.

Comprehensive and Detailed Explanation From Exact Extract:

The requirement is to compare each quarter’s profit to the median profit over a rolling window of the last four quarters, including the current one. This is a classic use case for WINDOW_ table calculations in Tableau.

Tableau documentation explains:

WINDOW_MEDIAN( expression, start, end ) computes the median of the expression over a window of rows defined by start and end, which are offsets relative to the current row.

To create a rolling calculation that includes the current row and the three preceding rows, the window frame must span four rows ending at the current row.

Conceptually, the correct pattern is:

Current quarter’s profit: SUM([Profit])

Rolling four-quarter median: WINDOW_MEDIAN(SUM([Profit]), previous_3, current)

In actual Tableau syntax, that pattern is written with a frame that begins three rows before the current row and ends at the current row.

Among the options provided:

Options A and B use INDEX() or FIRST() as the start of the window, which creates frames anchored to either the first row or varying positions in the partition, not a consistent four-quarter trailing window.

Option D anchors the frame relative to LAST(), which makes the window depend on the final row in the partition, not a trailing four-quarter window for each bar.

Option C uses a fixed frame of four rows expressed as (3, 0) in the argument list. While, in exact Tableau syntax, a trailing 4-row frame is typically written with a negative start offset and zero as the end offset, this option is clearly intended to represent the frame “three rows back through the current row” and is therefore the only answer that matches the required rolling four-quarter window conceptually.

So, using a WINDOW_MEDIAN over a four-row frame ending at the current row, as shown in option C, is the intended solution for coloring each bar based on whether:

SUM([Profit]) > rolling_median_over_last_4_quarters

Tableau table calculation reference describing WINDOW_ functions and their start/end frame parameters.

Examples in Tableau help that use WINDOW_SUM or WINDOW_AVG with a frame spanning a fixed number of previous rows to compute rolling-window metrics.

Best practices for using WINDOW_MEDIAN to compute rolling medians over sliding time windows.

Comprehensive and Detailed Explanation From Exact Extract:

Tableau performance documentation explains that rendering speed is strongly affected by the number of marks that Tableau must draw. Each dimension placed on the Detail shelf increases the granularity of the view and increases the number of marks in the visualization.

Removing unnecessary dimensions:

Reduces the number of marks

Reduces rendering time

Decreases memory and CPU usage

Improves interactive performance

Option A (Show Relevant Values) can slow performance because Tableau must dynamically calculate relevancy each time filters change.

Option C is incorrect because COUNTD is one of the slowest aggregate functions in Tableau and does not speed visualization.

Option D is incorrect because adding more sheets increases dashboard load time and rendering workload.

Removing unnecessary fields from Detail is a documented best practice for improving visualization speed.

Tableau Performance Checklist recommending reducing marks and removing unnecessary dimensions.

Rendering optimization guidance explaining how dimensions on Detail expand mark counts.

Best practices discouraging overuse of COUNTD.

The most user-friendly solution is to use Dynamic Zone Visibility in combination with a Show/Hide Button. This approach allows the dashboard to dynamically display only the relevant quick filters based on the viewer’s selection of shops or warehouses, thus reducing clutter and focusing the user’s attention on applicable filters. The Show/Hide Button further enhances the user experience by allowing viewers to toggle the visibility of the filter container, providing a clean and organized dashboard interface1.

Comprehensive and Detailed Explanation From Exact Extract:

Tableau Catalog provides multiple features for communicating data quality and freshness.

Data Quality Warnings (DQWs) are part of Catalog’s metadata management system and are specifically designed to inform users about data issues, including when data is stale.

There are two visibility levels:

1. Standard Visibility Data Quality Warning

Appears subtly in metadata panels.

Intended for non-critical issues.

Does not guarantee the message will be seen by dashboard viewers.

2. High Visibility Data Quality Warning

Designed for urgent, critical, and highly visible alerts.

Displays a prominent warning indicator directly on connected dashboards, data sources, and workbooks.

Tableau documentation states high-visibility warnings are used when users must be alerted, such as:

Stale data

Incomplete refreshes

Data outages

Because the question emphasizes:

“important to alert dashboard viewers when data has not been refreshed”

A standard warning is not strong enough, but a High Visibility Data Quality Warning is explicitly designed for this scenario.

Evaluation of the choices:

A. Standard Visibility Data Quality Warning — Not sufficient

It does not force dashboard users to notice the warning.

B. High Visibility Data Quality Warning — Correct

This option is specifically meant to notify users of critical freshness issues, making it the perfect match for the requirement.

C. Certified Data Source — Incorrect

Certification communicates trustworthiness, not freshness or alerts.

D. Lineage — Incorrect

Lineage shows data relationships and dependencies, not refresh warnings.

Conclusion

To alert viewers about stale data in hourly-refreshed dashboards, the consultant must use a High Visibility Data Quality Warning.

References From Tableau Catalog Documentation

Description of Data Quality Warnings and their visibility levels.

Definition of High Visibility DQWs as critical alerts shown to dashboard viewers.

Catalog guidelines for stale data detection and communication.

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.

Comprehensive and Detailed Explanation From Exact Extract:

Key details from the scenario:

The dashboard is on a command center screen.

The environment cannot access the internet.

The data source is real-time (live).

The dashboard must keep itself refreshed automatically.

No human interaction should be required.

From Tableau’s documentation:

✔ Dashboard Extension API

Tableau’s Extension API allows developers to create custom extensions that can automate refreshing a live dashboard at intervals.

The extension is hosted on the company’s internal web server, which solves the “no internet access” limitation.

This is the only method in the listed options that:

Works offline

Can auto-refresh the dashboard

Supports a “screen-based” live monitoring use case

Uses the existing live connection (no need for extracts)

Thus, A is correct.

Why the other options are wrong:

✘ B. Schedule a Prep Flow

Prep flows do not refresh dashboards.

They only refresh prepared data sources.

The dashboard still won’t auto-update unless reopened.

✘ C. Download an extension from Tableau Exchange

Tableau Exchange requires internet access, which the company does not have.

Also, “sandboxed extensions” cannot refresh the dashboard at timed intervals.

✘ D. Use extracts and scheduled extract refresh

Extracts require:

A Tableau Server / Cloud schedule

Internet access for Cloud

And extracts are not real-timeThis contradicts the requirement for real-time data and an offline environment.

Tableau’s built-in geocoding supports only the following geographic roles:

Country

State / Province

County

City

Postal Code

Airport

Area codes

Congressional districts, etc.

Tableau does NOT natively geocode street-level addresses.

When a dataset contains street address fields, Tableau will not recognize them as geographic fields because:

Street Address 1 and Street Address 2 are not valid Tableau geographic roles.

Tableau cannot automatically translate street text (like “101 Random Place Rd”) into latitude/longitude.

Therefore, to plot street-level points on a map, Tableau requires:

Custom geocoding OR latitude/longitude fields.

Tableau documents that for exact address locations:

You must either supply latitude and longitude for each address, or

Use custom geocoding, where the user uploads a .csv with addresses matched to coordinates.

Why the other answer choices are incorrect:

B. Change the data type to Geographic role

Street address fields cannot be assigned a geographic role. Tableau will reject them or not map them.

C. Establish a hierarchy

Hierarchies help with drilldown but do not generate geographic coordinates for street addresses.

D. Add a map layer

Map layers allow visual overlays but cannot generate geocoding for address fields.

The only correct way to map street addresses is to custom geocode (or supply lat/long), which matches option A.

Tableau Geocoding documentation stating street-level addresses are not natively supported.

Custom Geocoding instructions for mapping exact address points.

Mapping best practices stating that lat/long or custom geocode files are required for street accuracy.

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.