Comprehensive and Detailed Explanation From Exact Extract:
Tableau's RLS entitlement design patterns include:
1. Deepest Granularity Method
Requires one unique role one unique lowest-level value pairing.
Fails when the dataset contains duplicate lowest-level values (e.g., multiple ''East'' sub-regions across different regions).
Cannot operate correctly when some lowest-level values are NULL.
Thus, the deepest granularity method is not valid here.
2. Sparse Entitlements Method
Tableau documentation states:
Sparse entitlements define RLS at each level of the hierarchy instead of only at the lowest level.
This method supports duplicate lowest-level values.
Handles scenarios where some levels are NULL because higher-level entitlements (e.g. Region = AMER) can still correctly apply.
More flexible for hierarchical geographic structures (Region Sub-Region Country, etc.).
Given the client's dataset:
Multiple ''East'' and ''West'' sub-regions
Some ''Null'' sub-regions
Hierarchical levels present
Sparse entitlements is the only correct and supported choice.
Why the incorrect options are wrong:
A & B --- Deepest Granularity
Deepest granularity fails when the lowest-level values are not unique.
It cannot handle NULL values at the lowest tier.
Performance is not superior in this scenario.
D --- Sparse because it is most performant
Performance is not the defining advantage.
Flexibility and ability to handle duplicate lowest-level values is.
Thus, C is the correct statement.
RLS entitlement design patterns: deepest vs. sparse entitlements.
Rules requiring unique lowest-level identifiers for deepest granularity.
Guidance stating sparse entitlements should be used when duplicates or NULL values exist in hierarchical structures.
