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HCAHD Apache-Hadoop-Developer もっと多くの認可と就職機会を貰いたいのですか。

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Apache-Hadoop-Developer PDF DEMO:

QUESTION NO: 1
Which HDFS command copies an HDFS file named foo to the local filesystem as localFoo?
A. hadoop fs -get foo LocalFoo
B. hadoop -cp foo LocalFoo
C. hadoop fs -Is foo
D. hadoop fs -put foo LocalFoo
Answer: A

QUESTION NO: 2
Which one of the following statements describes a Pig bag. tuple, and map, respectively?
A. Unordered collection of maps, ordered collection of tuples, ordered set of key/value pairs
B. Unordered collection of tuples, ordered set of fields, set of key value pairs
C. Ordered set of fields, ordered collection of tuples, ordered collection of maps
D. Ordered collection of maps, ordered collection of bags, and unordered set of key/value pairs
Answer: B

QUESTION NO: 3
You are developing a MapReduce job for sales reporting. The mapper will process input keys representing the year (IntWritable) and input values representing product indentifies (Text).
Indentify what determines the data types used by the Mapper for a given job.
A. The key and value types specified in the JobConf.setMapInputKeyClass and
JobConf.setMapInputValuesClass methods
B. The data types specified in HADOOP_MAP_DATATYPES environment variable
C. The mapper-specification.xml file submitted with the job determine the mapper's input key and value types.
D. The InputFormat used by the job determines the mapper's input key and value types.
Answer: D
Explanation:
The input types fed to the mapper are controlled by the InputFormat used.
The default input format, "TextInputFormat," will load data in as (LongWritable, Text) pairs.
The long value is the byte offset of the line in the file. The Text object holds the string contents of the line of the file.
Note: The data types emitted by the reducer are identified by setOutputKeyClass() andsetOutputValueClass(). The data types emitted by the reducer are identified by setOutputKeyClass() and setOutputValueClass().
By default, it is assumed that these are the output types of the mapper as well. If this is not the case, the methods setMapOutputKeyClass() and setMapOutputValueClass() methods of the JobConf class will override these.
Reference: Yahoo! Hadoop Tutorial, THE DRIVER METHOD

QUESTION NO: 4
Which project gives you a distributed, Scalable, data store that allows you random, realtime read/write access to hundreds of terabytes of data?
A. HBase
B. Hue
C. Pig
D. Hive
E. Oozie
F. Flume
G. Sqoop
Answer: A
Explanation:
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)

QUESTION NO: 5
All keys used for intermediate output from mappers must:
A. Implement a splittable compression algorithm.
B. Be a subclass of FileInputFormat.
C. Implement WritableComparable.
D. Override isSplitable.
E. Implement a comparator for speedy sorting.
Answer: C
Explanation:
The MapReduce framework operates exclusively on <key, value> pairs, that is, the framework views the input to the job as a set of <key, value> pairs and produces a set of <key, value> pairs as the output of the job, conceivably of different types.
The key and value classes have to be serializable by the framework and hence need to implement the
Writable interface. Additionally, the key classes have to implement the WritableComparable interface to facilitate sorting by the framework.
Reference: MapReduce Tutorial

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Updated: May 27, 2022