Explanation:

The key phrase is "Tableau Data Management enabled." This license provides access to Tableau Catalog, which includes the powerful Lineage feature. Lineage automatically maps and visualizes the dependencies between all assets in your Tableau environment.

Here’s the precise process:

The consultant would navigate to the Tableau Server/Cloud site.
Go to the External Assets tab. This section of Catalog shows the upstream databases, tables, and files that feed your Tableau content.
Locate and select the specific database (or even a specific table within it) that is being migrated.
Click on the Lineage tab. This will display a visual diagram and a list of all downstream dependencies.
The view will explicitly list every single published data source, workbook, and virtual connection that uses this database. The consultant can quickly count the number of workbooks listed to understand the full impact of the migration.
This method is fast, comprehensive, and proactive, providing a complete picture before any change is made.

Why the Other Options are Incorrect:

B. Leverage the Tableau Developer API to query the workbooks' metadata. While technically possible, this is a complex, custom-programming solution. It requires writing and running a script to extract metadata from every workbook and then parse it to find the connections. This is not "quick" compared to the single-click solution provided by the built-in Catalog Lineage feature available to the consultant.

C. Complete the migration and let users report errors as they are noticed. This is a highly unprofessional and reactive approach. It causes unnecessary business disruption, user frustration, and potential downtime. A consultant's role is to plan and mitigate risk, not to create it.

D. Open each workbook and identify the data source. This is a completely manual, time-consuming, and error-prone process. On a server with dozens or hundreds of workbooks, this task is impractical and guarantees that some dependencies will be missed. It is the opposite of a "quick" solution.

Reference & Key Concepts:

Tableau Catalog: A feature of the Data Management add-on that provides data lineage, impact analysis, and data quality warnings.

Lineage: The ability to trace the path of data from its original source (External Assets) through published data sources and into workbooks.

Impact Analysis: The process of using lineage to understand which assets will be affected by a change to a data source, column, or other asset. This is a core capability of Catalog and a best practice for change management.

For the exam, remember that when Data Management is enabled, the Lineage tab in Catalog is the definitive and fastest tool for performing impact analysis before a data source migration or change.

Explanation:

{ FIXED [Order ID] : SUM([Sales]) } computes total sales per order (independent of the view).
{ FIXED [Region] : AVG( { FIXED [Order ID] : SUM([Sales]) } ) } computes, for each region, the average of those per-order totals.
Comparing them flags orders whose total is greater than the regional average order total—exactly what’s required.

Why others aren’t:
A. Compares row-level [Sales] to per-order totals (mismatch in granularity).
B. Compares per-order total to regional total sales, not the average per order.
D. Uses INCLUDE [Region] (view-dependent) and still averages the same inner value—doesn’t correctly fix the level like C does.

References:
FIXED LODs compute values at specified dimensions, independent of the view.
Overview of aggregation with LODs (how LODs reconcile with the view).

Explanation:

When a Tableau Server project’s permissions are locked, the top-level project permissions act as the authoritative source. If the “Apply to Nested Projects” option is not checked, nested projects can maintain custom permissions, which can lead to inconsistencies with the organization’s governance strategy.

By checking “Apply to Nested Projects”, Tableau immediately enforces the top-level project permissions across all nested projects. Any existing custom permissions in the nested projects are overwritten, ensuring that all nested content now complies with the top-level governance policy. This action is immediate, and users’ access rights are updated automatically based on the top-level project’s locked permissions.

❌ Why the other options are incorrect

❌ A. Current custom access will be maintained, but new custom permissions will not be granted
This is incorrect because checking “Apply to Nested Projects” does not preserve existing custom permissions. Tableau overwrites custom permissions to enforce the top-level project settings immediately, not just for new permissions.

❌ C. Users will be prompted to manually update permissions for all nested projects
This is incorrect. Tableau does not require user intervention to enforce permissions. The system automatically updates nested project permissions based on the locked top-level project settings.

❌ D. Users will be notified that they will automatically lose access to content after 30 days
This is incorrect. Tableau applies the permission changes immediately, and there is no 30-day grace period or notification delay.

Explanation:

Why C Is the Correct Choice

The requirements are: (1) row-level security by territory, (2) fully automated for a large team with high turnover, (3) no manual maintenance of hundreds of users, and (4) no Tableau Data Management license.

The only solution that satisfies all of these without Data Management is the classic entitlements-table approach (also called “user-based row-level security with a join”):

A simple entitlements table is maintained in the database (or CSV/Excel) with two columns: Username (or email) and Territory.
Join this table to the sales fact table on Territory.
Add a data source filter: [Username] = USERNAME() (or [Email] = USERNAME() in Tableau Cloud).
Publish this single secured data source.

When any sales rep opens the dashboard, Tableau automatically filters the data to only the rows matching their login—no groups, no manual user filters, no separate workbooks. New hires or territory reassignments are handled by updating one row in the entitlements table; no Tableau Server admin work is required.

Why the Other Options Are Incorrect

A. Create one group per territory and map groups
This works but fails the “automated + high turnover” requirement. With dozens or hundreds of territories and frequent reassignments, the admin would constantly be moving users between groups—extremely labor-intensive and error-prone.

B. Create separate workbooks per territory
This is the least scalable and most maintenance-heavy option. Every territory change requires republishing workbooks and updating project-level permissions. It also fragments content and prevents a single unified “Sales Dashboard.”

D. Create a user filter in the workbook and manually map each user
Workbook-level user filters require the workbook author to manually maintain a giant list of every sales rep and their territory inside the calculation (e.g., USERNAME() = "john.doe@company.com" AND [Territory] = "West" or a huge CASE statement). With high turnover this list becomes unmanageable within hours and is exactly what Tableau warns against.

References

Tableau Help → Row-Level Security with User Filters → “Entitlements table method (recommended when you do not have Data Management)”
Tableau Blueprint → Governance → “Managed Self-Service without Data Management” – explicitly recommends the join + data-source-filter pattern for large, dynamic teams.
Tableau Knowledge Base article #000029456: “Best practices for RLS with high user churn” – states the entitlements table is the only low-maintenance option when Data Management is not licensed.

Answer: C is the only scalable, automated, and supportable solution that meets all of the stated constraints.

Explanation 💡

The client's original calculation, {FIXED [Customer ID]: COUNTD([Order ID])}, correctly calculates the total number of orders for each customer across the entire dataset, regardless of the view's filters or dimensions. This is why the result is higher than expected. When this field is aggregated using AVG over MONTH(Order Date), Tableau is taking the average of these total lifetime orders for all customers who made a purchase in that month, not the average number of orders per customer for that specific month.

To meet the client's expectation of "average number of orders per customer per month, broken down by region," the calculation needs to be rewritten to consider both the month and the region.

The correct approach is to create a calculation that first determines the number of orders per customer within each region. The month-by-month breakdown will then be handled by the view itself.

The formula {FIXED [Customer ID], [Region]: COUNTD([Order ID])} is the key.

FIXED [Customer ID], [Region]: This tells Tableau to calculate the number of distinct orders (COUNTD([Order ID])) for each unique combination of Customer ID and Region. This creates a new column in the data source that contains the total number of orders per customer, scoped to their region.
When this new field is added to the view, which is already broken down by MONTH(Order Date) and Region, the AVG aggregation will correctly calculate the average of these pre-computed values for each month.

Why the other options are incorrect:

A. {INCLUDE [Customer ID]: COUNTD([Order ID])}: An INCLUDE LOD expression includes the specified dimensions in the calculation's granularity while still being affected by the view's filters. While this would consider Customer ID, it would still be aggregated by the dimensions already in the view (MONTH(Order Date) and Region), potentially leading to incorrect or unexpected results because it does not create a fixed, pre-aggregated number of orders per customer. The correct approach is to fix the aggregation at the customer/region level first.
C. {EXCLUDE [Customer ID]: COUNTD([Order ID])}: This calculation would count the distinct orders for each combination of dimensions in the view, excluding Customer ID. This would essentially count the total number of orders for each month and region, which is not what the client wants.
D. {FIXED [Customer ID], [Region], [Order Date]: COUNTD([Order ID])}: This calculation is too granular. It would count the number of orders per customer, per region, per specific order date. Since COUNTD([Order ID]) for a single day would usually be 1 (unless a customer places multiple orders on the same day with different IDs), this calculation would return a value of 1 for most instances, and the average would be around 1, which is not the expected outcome of average orders per customer per month. The correct granularity should be at the customer and region level to count the total orders, and then the view's dimensions (MONTH(Order Date)) should be used to slice the average.