Best Practices and Optimization of Hadoop MCQ question and answer with solution | Hadoop MCQs

A. To handle speculative workloads

B. To minimize resource usage

C. To mitigate the impact of slow-running tasks

D. To speed up task completion

A. Use the MapReduce framework for all joins

B. Use Hadoop Streaming for efficient joins

C. Optimize join keys and consider using Map-side joins

D. Increase the number of reducers

A. TaskTracker

B. NodeManager

C. ResourceManager

D. JobTracker

A. Monitors the liveliness of HDFS blocks

B. Monitors the health of task nodes

C. Monitors the health of the NameNode

D. Monitors the availability of MapReduce jobs

A. Increase the number of reducers

B. Decrease the number of reducers

C. Use a custom partitioner and combiner

D. Use default settings for shuffle

A. It helps in load balancing across reducers

B. It speeds up the entire job execution

C. It increases the replication factor of output data

D. It minimizes the use of speculative execution

A. By replicating data across multiple nodes

B. By increasing the block size of HDFS

C. By compressing data in HDFS

D. By decreasing the replication factor

A. To balance the load across Hadoop nodes

B. To optimize Hadoop's network performance

C. To allocate more resources to specific nodes

D. To compress data in HDFS

A. Increase the number of reducers

B. Decrease the number of reducers

C. Use a custom partitioner

D. Use a combiner function

A. mapreduce.task.max-attempts

B. mapreduce.task.attempt.max

C. mapreduce.task.max-retries

D. mapreduce.task.retry.max