Salesforce Analytics-Con-301 - Salesforce Certified Tableau Consultant

When “Apply to Nested Projects” is checked in Tableau Server, the permission rules set at the top-level project are enforced for all assets in the project and all nested projects. This means that any custom access previously granted to nested projects will be overridden, and the permissions will revert to those defined at the top-level project. This action ensures consistent application of the data governance strategy across all divisions.

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.

The correct calculation for classifying profit margins into categories based on specified thresholds involves the use of conditional statements that check ranges in a logical order:

Highly Profitable Classification: The first condition checks if the profit margin is 50% or more. This must use the ">=" operator to include exactly 50% as "Highly Profitable".

Profitable Classification: The next condition checks if the profit margin is between 0% and 50%. Since any value falling at or above 50% is already classified, this condition only needs to check for values greater than or equal to 0%.

Unprofitable Classification: The final condition captures any remaining scenarios, which would only be values less than 0%.

For optimal performance, it is recommended to limit the data to what is necessary for analysis, which in this case would be the current and previous years. Moving the calculation of averages to the data layer and aggregating the dates to a monthly level will reduce the granularity of the data, thereby improving the performance of the dashboard. This approach aligns with best practices for optimizing workbook performance in Tableau, which suggest simplifying the data model and reducing the number of records processed12.

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.

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.

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.

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.

To transform the Orderld field in Tableau Prep, the Split option is the most efficient and straightforward method. Here’s how you can apply it:

In Tableau Prep, drag your dataset into the flow.

Click on the Orderld field in the workspace to select it.

Look for the option in the toolbar that says "Split" and select it.

Choose "Automatic Split" if the delimiters (such as hyphens) are consistent; Tableau Prep should automatically detect the hyphen as the delimiter and split the Orderld into multiple new fields.

The dataset should now show new columns: one for the country code (CA, FR, US), one for the year (2017), and one for the order number (152156, 152157, etc.).

The Split option works effectively here because it automatically identifies and uses the hyphen as the delimiter to divide the original Orderld into the desired components without manual specification of conditions or writing any formulas.

References

This procedure is based on the standard functionalities provided in Tableau Prep for splitting a field into multiple columns based on a delimiter, as described in the Tableau Prep user guide.

Comprehensive and Detailed Explanation From Exact Extract:

Key Tableau Catalog behaviors:

Connected workbooks = any workbook linked to the published data source.

Impacted workbooks = only workbooks that use the specific field.

If a workbook connects to the data source but never uses the field, it appears as “connected” but not impacted.

This explains EXACTLY why:

7 workbooks are connected

Only 6 use the changed field

Therefore only 6 are impacted

This matches Option C.

Why the other options are incorrect:

A. Field used twice

Still counts as one workbook — does not explain discrepancy.

B. Permission issue

If permissions blocked visibility, the data source would not list 7 connections.

D. Custom SQL use

Catalog can still detect field usage through metadata lineage; Custom SQL does NOT hide workbook dependency.

Thus, only Option C logically explains the scenario.

Data Catalog lineage rules: “Connected vs. Impacted” distinction.

Field-level impact analysis documentation.

Workbook dependency logic within Tableau Catalog.