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Salesforce Analytics-Con-301 - Salesforce Certified Tableau Consultant

Last Update Feb 05, 2026

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Total Questions: 100
Free Practice Questions: 26

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?

Options:

A.

Replace the Multiple Values (Dropdown) filter with a Multiple Values (Custom List) filter.

B.

Replace the Relative Date filter with a Multiple Values (Dropdown) filter on YEAR([Created Date]).

C.

Replace the TOTAL([Opportunities]) calculation with a Grand Total.

D.

Replace SUM([Value]) with WINDOW_SUM([Value]).

Answer
B
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.

A shipping clerk wants to use a Sankey diagram to analyze the flow of goods between different categories, shipping modes, and locations to spot bottlenecks and optimize the most critical paths. The company uses Tableau Cloud.

How should the shipping clerk create a chart that depicts the above information?

Options:

A.

Search and download a suitable sandboxed Viz Extension from Tableau Exchange.

B.

Use Show Me to start a view based on the fields selected.

C.

Search and download a suitable sandboxed Accelerator from Tableau Exchange.

D.

Search and download a suitable Connector from Tableau Exchange.

Answer
A
Explanation

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.

A client wants to use a bar chart to visualize the trend in profit per quarter for the last 5 years. They want each bar's color to be determined by whether the profit during that quarter was greater than the median profit for the past four quarters, including the current quarter.

For example, if a bar represents profit for 2020 Q4, they want to visually see whether the profit for 2020 Q4 is greater than the median profit for 2020 Q1–2020 Q4.

Which table calculation should produce the desired result?

Options:

A.

SUM([Profit]) > WINDOW_MEDIAN(SUM([Profit]), INDEX(), INDEX() + 3)

B.

SUM([Profit]) > WINDOW_MEDIAN(SUM([Profit]), FIRST(), FIRST() + 3)

C.

SUM([Profit]) > WINDOW_MEDIAN(SUM([Profit]), 3, 0)

D.

SUM([Profit]) > WINDOW_MEDIAN(SUM([Profit]), LAST()-3, LAST())

Answer
C
Explanation

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.

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Salesforce Analytics-Con-301 FAQ'S

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