Hadoop Questions and Answers – Scaling out in Hadoop

This set of Hadoop Multiple Choice Questions & Answers (MCQs) focuses on “Scaling out in Hadoop”.

1. ________ systems are scale-out file-based (HDD) systems moving to more uses of memory in the nodes.
a) NoSQL
b) NewSQL
c) SQL
d) All of the mentioned
View Answer

Answer: a
Explanation: NoSQL systems make the most sense whenever the application is based on data with varying data types and the data can be stored in key-value notation.

2. Point out the correct statement.
a) Hadoop is ideal for the analytical, post-operational, data-warehouse-ish type of workload
b) HDFS runs on a small cluster of commodity-class nodes
c) NEWSQL is frequently the collection point for big data
d) None of the mentioned
View Answer

Answer: a
Explanation: Hadoop together with a relational data warehouse, they can form very effective data warehouse infrastructure.

3. Hadoop data is not sequenced and is in 64MB to 256MB block sizes of delimited record values with schema applied on read based on ____________
a) HCatalog
b) Hive
c) Hbase
d) All of the mentioned
View Answer

Answer: a
Explanation: Other means of tagging the values also can be used.

4. __________ are highly resilient and eliminate the single-point-of-failure risk with traditional Hadoop deployments.
a) EMR
b) Isilon solutions
c) AWS
d) None of the mentioned
View Answer

Answer: b
Explanation: enterprise data protection and security options including file system auditing and data-at-rest encryption to address compliance requirements are also provided by Isilon solution.

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5. Point out the wrong statement.
a) EMC Isilon Scale-out Storage Solutions for Hadoop combine a powerful yet simple and highly efficient storage platform
b) Isilon native HDFS integration means you can avoid the need to invest in a separate Hadoop infrastructure
c) NoSQL systems do provide high latency access and accommodate less concurrent users
d) None of the mentioned
View Answer

Answer: c
Explanation: NoSQL systems do provide low latency access and accommodate many concurrent users.

6. HDFS and NoSQL file systems focus almost exclusively on adding nodes to ____________
a) Scale out
b) Scale up
c) Both Scale out and up
d) None of the mentioned
View Answer

Answer: a
Explanation: HDFS and NoSQL file systems focus almost exclusively on adding nodes to increase performance (scale-out) but even they require node configuration with elements of scale up.

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7. Which is the most popular NoSQL database for scalable big data store with Hadoop?
a) Hbase
b) MongoDB
c) Cassandra
d) None of the mentioned
View Answer

Answer: a
Explanation: HBase is the Hadoop database: a distributed, scalable Big Data store that lets you host very large tables — billions of rows multiplied by millions of columns — on clusters built with commodity hardware.

8. The ___________ can also be used to distribute both jars and native libraries for use in the map and/or reduce tasks.
a) DataCache
b) DistributedData
c) DistributedCache
d) All of the mentioned
View Answer

Answer: c
Explanation: The child-jvm always has its current working directory added to the java.library.path and LD_LIBRARY_PATH.

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9. HBase provides ___________ like capabilities on top of Hadoop and HDFS.
a) TopTable
b) BigTop
c) Bigtable
d) None of the mentioned
View Answer

Answer: c
Explanation: Google Bigtable leverages the distributed data storage provided by the Google File System.

10. __________ refers to incremental costs with no major impact on solution design, performance and complexity.
a) Scale-out
b) Scale-down
c) Scale-up
d) None of the mentioned
View Answer

Answer: c
Explanation: Adding more CPU/RAM/Disk capacity to Hadoop DataNode that is already part of a cluster does not require additional network switches.

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If you find a mistake in question / option / answer, kindly take a screenshot and email to [email protected]

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Manish Bhojasia - Founder & CTO at Sanfoundry
Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He lives in Bangalore, and focuses on development of Linux Kernel, SAN Technologies, Advanced C, Data Structures & Alogrithms. Stay connected with him at LinkedIn.

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