Apache Hadoop Developer

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The dumps for Apache Hadoop Developer exam was last updated on May 13,2025 .

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Question#1

A combiner reduces:

A. The number of values across different keys in the iterator supplied to a single reduce method call.

B. The amount of intermediate data that must be transferred between the mapper and reducer.

C. The number of input files a mapper must process.

D. The number of output files a reducer must produce.

Explanation:
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 & Answers for Hadoop MapReduce developers, What are combiners? When should I use a combiner in my MapReduce Job?

Question#2

You have just executed a MapReduce job. Where is intermediate data written to after being emitted from the Mapper’s map method?

A. Intermediate data in streamed across the network from Mapper to the Reduce and is never written to disk.

B. Into in-memory buffers on the TaskTracker node running the Mapper that spill over and are written into HDF

C. Into in-memory buffers that spill over to the local file system of the TaskTracker node running the Mapper.

D. Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker node running the Reducer

E. Into in-memory buffers on the TaskTracker node running the Reducer that spill over and are written into HDF

Explanation:
The mapper output (intermediate data) is stored on the Local file system (NOT HDFS) of each individual mapper nodes. This is typically a temporary directory location which can be setup in config by the hadoop administrator. The intermediate data is cleaned up after the Hadoop Job completes.
Reference: 24 Interview Questions & Answers for Hadoop MapReduce developers, Where is the Mapper Output (intermediate kay-value data) stored?

Question#3

Indentify the utility that allows you to create and run MapReduce jobs with any executable
or script as the mapper and/or the reducer?

A. Oozie

B. Sqoop

C. Flume

D. Hadoop Streaming

E. mapred

Explanation:
Hadoop streaming is a utility that comes with the Hadoop distribution. The utility allows you to create and run Map/Reduce jobs with any executable or script as the mapper and/or the reducer.
Reference: http://hadoop.apache.org/common/docs/r0.20.1/streaming.html (Hadoop Streaming, second sentence)

Question#4

You use the hadoop fs Cput command to write a 300 MB file using and HDFS block size of 64 MB.
Just after this command has finished writing 200 MB of this file, what would another user see when trying to access this life?

A. They would see Hadoop throw an ConcurrentFileAccessException when they try to access this file.

B. They would see the current state of the file, up to the last bit written by the command.

C. They would see the current of the file through the last completed block.

D. They would see no content until the whole file written and closed.

Question#5

What is a SequenceFile?

A. A SequenceFile contains a binary encoding of an arbitrary number of homogeneous writable objects.

B. A SequenceFile contains a binary encoding of an arbitrary number of heterogeneous writable objects.

C. A SequenceFile contains a binary encoding of an arbitrary number of WritableComparable objects, in sorted order.

D. A SequenceFile contains a binary encoding of an arbitrary number key-value pairs. Each key must be the same type. Each value must be same type.

Explanation:
SequenceFile is a flat file consisting of binary key/value pairs.
There are 3 different SequenceFile formats:
Uncompressed key/value records.
Record compressed key/value records - only 'values' are compressed here.
Block compressed key/value records - both keys and values are collected in 'blocks' separately and compressed. The size of the 'block' is configurable.
Reference: http://wiki.apache.org/hadoop/SequenceFile

Exam Code: Apache Hadoop Developer Q & A: 108 Q&As Updated: May 13,2025

Full Apache Hadoop Developer Exam Dumps Here

Exam Code: Apache Hadoop Developer
Q & A: 108 Q&As
Updated: May 13,2025

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