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

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Each section contains 100 MCQs on Best Practices and Optimization of Hadoop .

How can Hadoop Namenode High Availability (HA) be achieved?

A) By configuring multiple active Namenodes B) By using backup Namenodes C) By replicating the Namenode metadata D) By compressing Namenode data

Discuss

Answer & Solution

Answer: Option A

No explanation is given for this question. Let's Discuss on Board

Which Hadoop configuration property controls the maximum number of containers a node can run concurrently?

A) yarn.nodemanager.resource.memory-mb B) yarn.nodemanager.container-max-heap-mb C) yarn.scheduler.maximum-allocation-mb D) yarn.scheduler.maximum-container-mb

Discuss

Answer & Solution

Answer: Option C

No explanation is given for this question. Let's Discuss on Board

In Hadoop, what is the purpose of the Fair Scheduler's pool concept?

A) To group jobs based on priority B) To segregate tasks based on input size C) To allocate resources based on task complexity D) To randomly assign resources to tasks

Discuss

Answer & Solution

Answer: Option A

No explanation is given for this question. Let's Discuss on Board

How can the Hadoop job's input split size impact job performance?

A) Larger splits can improve data locality B) Smaller splits lead to faster processing C) Split size has no impact on job performance D) Larger splits cause increased network traffic

Discuss

Answer & Solution

Answer: Option B

No explanation is given for this question. Let's Discuss on Board

What is Hadoop's speculative execution policy for reducing job completion times?

A) Speculatively run tasks that are likely to finish slower B) Speculatively run tasks that are likely to finish faster C) Speculatively run tasks randomly D) Do not use speculative execution

Discuss

Answer & Solution

Answer: Option A

No explanation is given for this question. Let's Discuss on Board

How does the Hadoop Fair Scheduler allocate resources among jobs in a fair manner?

A) Based on the configured weights for each job B) Based on the job's input size C) Based on the job's execution time D) Randomly assigns resources to jobs

Discuss

Answer & Solution

Answer: Option A

No explanation is given for this question. Let's Discuss on Board

What is the purpose of Hadoop's Block Placement Policy?

A) Determines where to place the first replica B) Balances the number of replicas per block C) Controls block replication across the cluster D) Manages block deletion in HDFS

Discuss

Answer & Solution

Answer: Option C

No explanation is given for this question. Let's Discuss on Board

How can speculative execution be controlled at the job level in Hadoop?

A) Set "mapred.map.tasks.speculative.execution" to true B) Set "mapred.reduce.tasks.speculative.execution" to true C) Use the "mapred.speculative.execution" configuration D) Speculative execution cannot be controlled

Discuss

Answer & Solution

Answer: Option C

No explanation is given for this question. Let's Discuss on Board

What is the role of the Hadoop Secondary NameNode?

A) Takes periodic checkpoints of the HDFS metadata B) Manages Hadoop's secondary storage C) Acts as a backup Namenode in case of failures D) Optimizes MapReduce job performance

Discuss

Answer & Solution

Answer: Option A

No explanation is given for this question. Let's Discuss on Board

How can the replication factor of Hadoop HDFS impact performance and fault tolerance?

A) Higher replication improves fault tolerance B) Lower replication reduces network overhead C) Replication factor has no impact on performance D) Replication factor is only for backup

Discuss

Answer & Solution

Answer: Option A

No explanation is given for this question. Let's Discuss on Board

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