Cloudera CCD-410 dumps - 100% Pass Guarantee!

Correct Answer: A

Reference: 24 Interview Questions and Answers for Hadoop MapReduce developers, How the Client communicates with HDFS?

Correct Answer: C

Multiple reducers and total ordering

If your sort job runs with multiple reducers (either because mapreduce.job.reduces in mapred- site.xml has been set to a number larger than 1, or because you've used the -r option to specify the number of reducers on the command-line), then by default Hadoop will use the HashPartitioner to distribute records across the reducers. Use of the HashPartitioner means that you can't concatenate your output files to create a single sorted output file. To do this you'll need total ordering,

Reference: Sorting text files with MapReduce

Correct Answer: A

Combiners are used to increase the efficiency of a MapReduce program. They are used to aggregate intermediate map output locally on individual mapper outputs. Combiners can help you reduce the amount of data that needs to be transferred across to the reducers. You can use your reducer code as a combiner if the operation performed is commutative and associative. The execution of combiner is not guaranteed, Hadoop may or may not execute a combiner. Also, if required it may execute it more then 1 times. Therefore your MapReduce jobs should not depend on the combiners execution.

Reference: 24 Interview Questions and Answers for Hadoop MapReduce developers, What are combiners? When should I use a combiner in my MapReduce Job?

Correct Answer: A

Use Apache HBase when you need random, realtime read/write access to your Big Data. Note: This project's goal is the hosting of very large tables -- billions of rows X millions of columns -- atop clusters of commodity hardware. Apache HBase is an open-source, distributed, versioned, column- oriented store modeled after Google's Bigtable: A Distributed Storage System for Structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, Apache HBase provides Bigtable-like capabilities on top of Hadoop and HDFS.

Features

Linear and modular scalability.

Strictly consistent reads and writes.

Automatic and configurable sharding of tables

Automatic failover support between RegionServers.

Convenient base classes for backing Hadoop MapReduce jobs with Apache HBase tables.

Easy to use Java API for client access.

Block cache and Bloom Filters for real-time queries.

Query predicate push down via server side Filters

Thrift gateway and a REST-ful Web service that supports XML, Protobuf, and binary data encoding options

Extensible jruby-based (JIRB) shell

Support for exporting metrics via the Hadoop metrics subsystem to files or Ganglia; or via JMX

Reference: http://hbase.apache.org/ (when would I use HBase? First sentence)

Correct Answer: F

Sqoop ("SQL-to-Hadoop") is a straightforward command-line tool with the following capabilities: Imports individual tables or entire databases to files in HDFS Generates Java classes to allow you to interact with your imported data Provides the ability to import from SQL databases straight into your Hive data warehouse Note: Data Movement Between Hadoop and Relational Databases Data can be moved between Hadoop and a relational database as a bulk data transfer, or relational tables

can be accessed from within a MapReduce map function. Note:

* Cloudera's Distribution for Hadoop provides a bulk data transfer tool (i.e., Sqoop) that imports individual tables or entire databases into HDFS files. The tool also generates Java classes that support interaction with the imported data. Sqoop supports all relational databases over JDBC, and Quest Software provides a connector (i.e., OraOop) that has been optimized for access to data residing in Oracle databases.

Reference: http://log.medcl.net/item/2011/08/hadoop-and-mapreduce-big-data-analytics-gartner/ (Data Movement between hadoop and relational databases, second paragraph)