1. What does YARN stand for in Hadoop?
Answer:
Explanation:
YARN stands for Yet Another Resource Negotiator. It is a key component of Hadoop that manages and schedules resources across the cluster.
2. In Hadoop YARN, the Resource Manager's primary responsibility is:
Answer:
Explanation:
In YARN, the Resource Manager is responsible for allocating resources and scheduling tasks across the cluster.
3. Which component acts as the per-application "master" in YARN?
Answer:
Explanation:
The ApplicationMaster is responsible for the execution of a single application in YARN, acting as the per-application "master".
4. What is the role of NodeManager in Hadoop YARN?
Answer:
Explanation:
NodeManager is a per-node agent responsible for containers, monitoring their resource usage, and reporting the same to the ResourceManager.
5. Which of the following best describes a Container in YARN?
Answer:
Explanation:
In YARN, a Container represents a collection of physical resources such as memory, CPU, disk, and network allocated for running application-specific tasks.
6. How does YARN improve upon the classic MapReduce model?
Answer:
Explanation:
YARN enhances the MapReduce model by adding a more powerful and flexible resource management layer, allowing for more efficient utilization of resources.
7. In Hadoop YARN, the main function of the Scheduler is to:
Answer:
Explanation:
The Scheduler is responsible for allocating resources to the various running applications subject to constraints of capacities, queues, etc.
8. Which YARN component is responsible for tracking the status of running applications?
Answer:
Explanation:
The ApplicationMaster tracks the status and progress of its application, handling any requests for additional resources from the ResourceManager.
9. How does YARN handle failure of the ApplicationMaster?
Answer:
Explanation:
In case of ApplicationMaster failure, YARN's ResourceManager automatically restarts the ApplicationMaster to ensure the application's continued execution.
10. What type of scheduling does YARN offer?
Answer:
Explanation:
YARN offers two primary types of scheduling: Fair scheduling, which allocates resources to applications fairly, and Capacity scheduling, which allocates based on predefined capacities.
11. Which statement best describes the purpose of YARN's ResourceManager?
Answer:
Explanation:
The ResourceManager in YARN is primarily responsible for allocating system resources to the various running applications and managing the cluster's computing resources.
12. In YARN, what mechanism is used to monitor and report the health of the cluster nodes?
Answer:
Explanation:
The NodeManager in each node monitors the node's health and resource usage, reporting this information back to the ResourceManager.
13. What is the role of the ApplicationMaster in YARN?
Answer:
Explanation:
The ApplicationMaster negotiates resources with the ResourceManager and works with the NodeManagers to execute and monitor tasks.
14. Which feature is a key advantage of YARN over traditional MapReduce?
Answer:
Explanation:
YARN provides multi-tenancy, allowing multiple data processing engines like interactive SQL, real-time streaming, data science, and batch processing to handle data stored in a single platform.
15. How does the ResourceManager handle scalability in a YARN cluster?
Answer:
Explanation:
The ResourceManager scales the YARN cluster by managing multiple NodeManagers across the cluster, which handle the execution of tasks on individual nodes.
16. In YARN, what is the responsibility of the ApplicationMaster?
Answer:
Explanation:
The ApplicationMaster is responsible for scheduling tasks on various NodeManagers and monitoring their execution.
17. What is the primary function of the NodeManager's Container in YARN?
Answer:
Explanation:
The Container in the NodeManager provides an isolated environment for executing specific application tasks with allocated resources like CPU and memory.
18. Which one of the following is a key feature of YARN's ResourceManager?
Answer:
Explanation:
The ResourceManager in YARN is crucial for negotiating and allocating resources among the applications running in the cluster.
19. How does YARN enhance the processing capabilities of a Hadoop cluster?
Answer:
Explanation:
YARN enhances processing capabilities by enabling more efficient utilization of computational resources, allowing for diverse workloads and better performance.
20. In YARN, which component is responsible for keeping track of the heartbeat and health of the cluster nodes?
Answer:
Explanation:
The NodeManager keeps track of the health and status of the cluster nodes by sending regular heartbeats to the ResourceManager.
21. What is the primary benefit of YARN's decoupling of the programming model from the resource management infrastructure?
Answer:
Explanation:
Decoupling the programming model from resource management in YARN allows for higher resource utilization and greater flexibility in running various types of applications.
22. Which statement correctly describes the function of the NodeManager in YARN?
Answer:
Explanation:
The NodeManager in YARN manages the execution of tasks on an individual node, handling container management and task monitoring.
23. YARN's ApplicationMaster and ResourceManager communicate with each other for:
Answer:
Explanation:
The ApplicationMaster communicates with the ResourceManager for resource allocation, and with the NodeManager for task execution and monitoring.
24. How does YARN contribute to data processing efficiency in a Hadoop cluster?
Answer:
Explanation:
YARN contributes to data processing efficiency by allowing different data processing engines, such as MapReduce, Spark, and Tez, to run effectively on the same Hadoop cluster, thereby facilitating diverse and efficient data processing workloads.
25. In the context of YARN, what is the significance of the Capacity Scheduler?
Answer:
Explanation:
The Capacity Scheduler in YARN is designed to manage resource allocation, ensuring that various applications and users get their fair share of resources in the cluster, as defined by the capacities of queues.