Yet Another Resource Negotiator (YARN) DATA-SCIENCE-OPTIMIZE Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   What is a key improvement of YARN-based MapReduce over classic MapReduce?

   • AYARN requires a dedicated cluster for each application
   • BYARN eliminates the need for HDFS
   • CYARN uses a single daemon for all cluster management
   • DYARN allows multiple applications to share cluster resources
   Reveal Solution Discussion Select an answer first
2. 17foundation · easy

   What happens if the ApplicationMaster fails in YARN?

   • AThe NodeManager takes over the ApplicationMaster's role
   • BThe ResourceManager restarts the ApplicationMaster
   • CThe application is terminated immediately
   • DThe client must resubmit the application
   Reveal Solution Discussion Select an answer first
4. 18foundation · easy

   What is the primary responsibility of the YARN ResourceManager?

   • AManaging the cluster's resources and scheduling applications
   • BStoring application data in HDFS
   • CExecuting map and reduce tasks
   • DMonitoring the health of individual tasks
   Reveal Solution Discussion Select an answer first
5. 19foundation · easy

   In classic MapReduce, which component was responsible for both resource management and job scheduling?

   • ANameNode
   • BApplicationMaster
   • CResourceManager
   • DJobTracker
   Reveal Solution Discussion Select an answer first
6. 20application · medium

   A company is migrating from classic MapReduce (MRv1) to YARN-based MapReduce (MRv2). The team wants to understand the key architectural difference that allows YARN to support multiple processing engines (e.g., MapReduce, Spark, Tez) on the same cluster. What is the primary architectural change in YARN that enables this?

   • AYARN separates resource management (ResourceManager/NodeManager) from job execution (ApplicationMaster), allowing different frameworks to implement their own ApplicationMasters.
   • BYARN moves all job scheduling logic into the NodeManager, allowing each node to independently schedule tasks.
   • CYARN uses a shared filesystem to store all application code, eliminating the need for per-framework execution engines.
   • DYARN replaces the JobTracker with a single unified ApplicationMaster that can run any type of job.
   Reveal Solution Discussion Select an answer first