YARN & MapReduce interview questions

1. A.a YARN resource lease of memory and CPU on a node in which an application runs a task
2. B.the optional MapReduce function that pre-aggregates map output locally before the shuffle to cut network traffic to reducers
3. C.the YARN master daemon that arbitrates cluster resources among applications via its scheduler
4. D.a YARN scheduler that divides the cluster into hierarchical queues with guaranteed capacities and elastic sharing

Answer + AI explanation with Pro

1. A.fair scheduler
2. B.Speculative task
3. C.ResourceManager
4. D.Capacity Scheduler

Answer + AI explanation with Pro

1. A.ResourceManager — the MapReduce stage that partitions, sorts, and transfers map outputs to the reducers that consume them
2. B.ResourceManager — a per-application YARN process that negotiates containers from the ResourceManager and coordinates task execution
3. C.ResourceManager — the YARN master daemon that arbitrates cluster resources among applications via its scheduler
4. D.ResourceManager — a YARN scheduler that divides the cluster into hierarchical queues with guaranteed capacities and elastic sharing

Answer + AI explanation with Pro

1. A.the YARN scheduler that partitions cluster resources into hierarchical queues with guaranteed capacities and elastic sharing of idle resources
2. B.a per-application YARN process that negotiates containers from the ResourceManager and coordinates task execution
3. C.the per-application YARN process that negotiates containers from the ResourceManager and coordinates task execution for that job
4. D.a YARN scheduler that dynamically balances resources so all running applications get, on average, an equal share over time

Answer + AI explanation with Pro

1. A.Shuffle phase
2. B.Speculative task
3. C.Container
4. D.ApplicationMaster

Answer + AI explanation with Pro

1. A.ApplicationMaster — a per-application YARN process that negotiates containers from the ResourceManager and coordinates task execution
2. B.ApplicationMaster — the optional MapReduce function that pre-aggregates map output locally before the shuffle to cut network traffic to reducers
3. C.ApplicationMaster — a YARN feature that tags nodes, such as GPU machines, so queues can be restricted to run only on matching hardware
4. D.ApplicationMaster — the YARN feature tagging nodes (such as GPU or high-memory) so applications can request scheduling onto specific hardware partitions

Answer + AI explanation with Pro

1. A.a YARN scheduler action that reclaims containers from over-served queues to satisfy starved queues below their guaranteed capacity
2. B.a YARN resource lease of memory and CPU on a node in which an application runs a task
3. C.a YARN scheduler that divides the cluster into hierarchical queues with guaranteed capacities and elastic sharing
4. D.a YARN scheduler that dynamically balances resources so all running applications get, on average, an equal share over time

Answer + AI explanation with Pro

1. A.Container
2. B.ResourceManager
3. C.Capacity Scheduler
4. D.Node labels

Answer + AI explanation with Pro

1. A.Container — the YARN feature tagging nodes (such as GPU or high-memory) so applications can request scheduling onto specific hardware partitions
2. B.Container — the optional MapReduce function that pre-aggregates map output locally before the shuffle to cut network traffic to reducers
3. C.Container — a YARN scheduler that dynamically balances resources so all running applications get, on average, an equal share over time
4. D.Container — a YARN resource lease of memory and CPU on a node in which an application runs a task

Answer + AI explanation with Pro

1. A.an optional MapReduce mini-reducer that aggregates map output locally to cut the data shuffled across the network
2. B.a YARN scheduler that dynamically balances resources so all running applications get, on average, an equal share over time
3. C.a YARN scheduler that divides the cluster into hierarchical queues with guaranteed capacities and elastic sharing
4. D.the YARN scheduler that partitions cluster resources into hierarchical queues with guaranteed capacities and elastic sharing of idle resources

Answer + AI explanation with Pro

1. A.combiner
2. B.Combiner
3. C.Fair Scheduler
4. D.Capacity Scheduler

Answer + AI explanation with Pro

1. A.Capacity Scheduler — the YARN scheduler that dynamically balances resources so all running applications get, on average, an equal share over time
2. B.Capacity Scheduler — a YARN scheduler action that reclaims containers from over-served queues to satisfy starved queues below their guaranteed capacity
3. C.Capacity Scheduler — a YARN scheduler that divides the cluster into hierarchical queues with guaranteed capacities and elastic sharing
4. D.Capacity Scheduler — a YARN scheduler that dynamically balances resources so all running applications get, on average, an equal share over time

Answer + AI explanation with Pro

1. A.a YARN scheduler that dynamically balances resources so all running applications get, on average, an equal share over time
2. B.a YARN scheduler action that reclaims containers from over-served queues to satisfy starved queues below their guaranteed capacity
3. C.the per-application YARN process that negotiates containers from the ResourceManager and coordinates task execution for that job
4. D.the MapReduce stage that partitions, sorts, and transfers map outputs to the reducers that consume them

Answer + AI explanation with Pro

1. A.Fair Scheduler
2. B.fair scheduler
3. C.Capacity Scheduler
4. D.combiner

Answer + AI explanation with Pro

1. A.Fair Scheduler — the optional MapReduce function that pre-aggregates map output locally before the shuffle to cut network traffic to reducers
2. B.Fair Scheduler — a YARN feature that tags nodes, such as GPU machines, so queues can be restricted to run only on matching hardware
3. C.Fair Scheduler — a YARN scheduler that dynamically balances resources so all running applications get, on average, an equal share over time
4. D.Fair Scheduler — the YARN scheduler that partitions cluster resources into hierarchical queues with guaranteed capacities and elastic sharing of idle resources

Answer + AI explanation with Pro

1. A.the YARN feature tagging nodes (such as GPU or high-memory) so applications can request scheduling onto specific hardware partitions
2. B.the MapReduce stage that partitions, sorts, and transfers map outputs to the reducers that consume them
3. C.the YARN scheduler that dynamically balances resources so all running applications get, on average, an equal share over time
4. D.an optional MapReduce mini-reducer that aggregates map output locally to cut the data shuffled across the network

Answer + AI explanation with Pro

1. A.Container
2. B.ApplicationMaster
3. C.Shuffle phase
4. D.capacity scheduler

Answer + AI explanation with Pro

1. A.Shuffle phase — a YARN scheduler that divides the cluster into hierarchical queues with guaranteed capacities and elastic sharing
2. B.Shuffle phase — a YARN resource lease of memory and CPU on a node in which an application runs a task
3. C.Shuffle phase — the MapReduce stage that partitions, sorts, and transfers map outputs to the reducers that consume them
4. D.Shuffle phase — the YARN master daemon that arbitrates cluster resources among applications via its scheduler

Answer + AI explanation with Pro

1. A.a per-application YARN process that negotiates containers from the ResourceManager and coordinates task execution
2. B.an optional MapReduce mini-reducer that aggregates map output locally to cut the data shuffled across the network
3. C.the optional MapReduce function that pre-aggregates map output locally before the shuffle to cut network traffic to reducers
4. D.a MapReduce duplicate of a slow task launched on another node so the faster attempt can finish the work

Answer + AI explanation with Pro

1. A.Combiner
2. B.fair scheduler
3. C.ResourceManager
4. D.capacity scheduler

Answer + AI explanation with Pro

1. A.Combiner — the MapReduce stage that partitions, sorts, and transfers map outputs to the reducers that consume them
2. B.Combiner — an optional MapReduce mini-reducer that aggregates map output locally to cut the data shuffled across the network
3. C.Combiner — the optional MapReduce function that pre-aggregates map output locally before the shuffle to cut network traffic to reducers
4. D.Combiner — the YARN scheduler that dynamically balances resources so all running applications get, on average, an equal share over time

Answer + AI explanation with Pro

1. A.a YARN resource lease of memory and CPU on a node in which an application runs a task
2. B.a YARN feature that tags nodes, such as GPU machines, so queues can be restricted to run only on matching hardware
3. C.a YARN scheduler that divides the cluster into hierarchical queues with guaranteed capacities and elastic sharing
4. D.a per-application YARN process that negotiates containers from the ResourceManager and coordinates task execution

Answer + AI explanation with Pro

1. A.capacity scheduler
2. B.Preemption
3. C.Combiner
4. D.Node labels

Answer + AI explanation with Pro

1. A.Node labels — a YARN feature that tags nodes, such as GPU machines, so queues can be restricted to run only on matching hardware
2. B.Node labels — a per-application YARN process that negotiates containers from the ResourceManager and coordinates task execution
3. C.Node labels — the per-application YARN process that negotiates containers from the ResourceManager and coordinates task execution for that job
4. D.Node labels — the YARN master daemon that arbitrates cluster resources among applications via its scheduler

Answer + AI explanation with Pro

1. A.a MapReduce duplicate of a slow task launched on another node so the faster attempt can finish the work
2. B.the per-application YARN process that negotiates containers from the ResourceManager and coordinates task execution for that job
3. C.the YARN scheduler that dynamically balances resources so all running applications get, on average, an equal share over time
4. D.a YARN scheduler action that reclaims containers from over-served queues to satisfy starved queues below their guaranteed capacity

Answer + AI explanation with Pro

1. A.Preemption
2. B.Fair Scheduler
3. C.capacity scheduler
4. D.combiner

Answer + AI explanation with Pro

1. A.Preemption — a YARN scheduler action that reclaims containers from over-served queues to satisfy starved queues below their guaranteed capacity
2. B.Preemption — the per-application YARN process that negotiates containers from the ResourceManager and coordinates task execution for that job
3. C.Preemption — a YARN resource lease of memory and CPU on a node in which an application runs a task
4. D.Preemption — a YARN scheduler that divides the cluster into hierarchical queues with guaranteed capacities and elastic sharing

Answer + AI explanation with Pro

1. A.the MapReduce stage that partitions, sorts, and transfers map outputs to the reducers that consume them
2. B.a per-application YARN process that negotiates containers from the ResourceManager and coordinates task execution
3. C.a MapReduce duplicate of a slow task launched on another node so the faster attempt can finish the work
4. D.the per-application YARN process that negotiates containers from the ResourceManager and coordinates task execution for that job

Answer + AI explanation with Pro

1. A.Node labels
2. B.combiner
3. C.Speculative task
4. D.Combiner

Answer + AI explanation with Pro

1. A.Speculative task — a YARN scheduler that divides the cluster into hierarchical queues with guaranteed capacities and elastic sharing
2. B.Speculative task — the per-application YARN process that negotiates containers from the ResourceManager and coordinates task execution for that job
3. C.Speculative task — an optional MapReduce mini-reducer that aggregates map output locally to cut the data shuffled across the network
4. D.Speculative task — a MapReduce duplicate of a slow task launched on another node so the faster attempt can finish the work

Answer + AI explanation with Pro