If there are only Default, QueueA, and QueueB subqueues in the YARN cluster, then their capacity can be set to 60%, 25%, and 22%, respectively.

The statement is false because it violates a fundamental configuration rule of the YARN CapacityScheduler. The sum of the capacities for all direct child queues under a parent queue must be exactly 100%. In this scenario, the Default, QueueA, and QueueB queues are sibling queues, typically under the root queue. The proposed capacity allocation is 60% + 25% + 22%, which totals 107%. Since this sum exceeds 100%, the YARN ResourceManager would fail to start with such a configuration, rendering it invalid.

The option "True" is incorrect. A configuration where the sum of sibling queue capacities is not equal to 100% is not permissible in YARN's CapacityScheduler. The scheduler's logic for resource allocation is predicated on this 100% total, and any deviation would make the configuration invalid and prevent the service from operating correctly.

1. Official Vendor Documentation (Apache Hadoop): The official documentation for the Apache Hadoop YARN CapacityScheduler explicitly states this constraint.

Source: Apache Hadoop 3.3.6 Documentation, YARN ResourceManager, CapacityScheduler.

Reference: In the section describing the setup of queues, it states: "The sum of capacities of all queues at each level must be equal to 100%." This rule applies to the children of the root queue and any other parent queue in the hierarchy.

Link: https://hadoop.apache.org/docs/r3.3.6/hadoop-yarn/hadoop-yarn-site/CapacityScheduler.html#QueueProperties (Refer to the description of the capacity property).

2. Official Vendor Documentation (Huawei): Huawei's FusionInsight, which is the platform for the H13-711 exam, is built upon Apache Hadoop and adheres to the same principles.

Source: Huawei FusionInsight HD 6.5.1 Product Documentation.

Reference: In the "YARN Configuration" or "Scheduler Configuration" section of the administrator guide, the parameters for capacity-scheduler.xml are detailed. The description for yarn.scheduler.capacity.root..capacity specifies that the sum of capacities for all queues at the same level must be 100.

3. University Courseware: Reputable university courses on big data systems confirm this fundamental principle of YARN resource management.

Source: Cornell University, CS 5412: Cloud Computing, Lecture on YARN.

Reference: Lecture slides and notes on YARN scheduling explicitly describe the hierarchical queue structure of the CapacityScheduler and the constraint that the sum of capacities of child queues must equal 100% of the parent's capacity. This ensures a deterministic and complete allocation of the parent's resources among its children.