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Cloudera CCD-333 Exam Syllabus Topics:

Cloudera Certified Developer for Apache Hadoop Sample Questions:

Question 1

Which of the following best describes the map method input and output?

A. It accepts a single key-value pair as input and can emit any number of key-value pairs as output, including zero.
B. It accepts a list of key-value pairs as input hut run emit only one key value pair as output.
C. It accepts a single key-value pair as input and can emit only one key-value pair as output.
D. It accepts a single key-value pair as input and emits a single key and list of corresponding values as output

Question 2

Custom programmer-defined counters in MapReduce are:

A. Lightweight devices for synchronization within MapReduce programs. You can use counters to coordinate execution between a mapper and a reducer.
B. Lightweight devices for ensuring the correctness of a MapReduce program. Mappers Increment counters, and reducers decrement counters. If at the end of the program the counters read zero, then you are sure that the job completed correctly.
C. Lightweight devices for bookkeeping within MapReduce programs.

Question 3

Given a Mapper, Reducer, and Driver class packaged into a jar, which is the correct way of submitting the job to the cluster?

A. hadoop jar class MyJar.jar MyDriverClass inputdir outputdir
B. jar MyJar.jar
C. jar MyJar.jar MyDriverClass inputdir outputdir
D. hadoop jar MyJar.jar MyDriverClass inputdir outputdir

Question 4

What is the standard configuration of slave nodes in a Hadoop cluster?

A. Each slave node runs a TaskTracker, but only a fraction of the slave nodes run DataNode daemons.
B. Each slave node either runs a TaskTracker or a DataNode daemon, but not both.
C. Each slave node runs a DataNode daemon, but only a fraction of the slave nodes run TaskTrackers.
D. Each slave node runs a JobTracker and a DataNode daemon.
E. Each slave node runs a TaskTracker and a DataNode daemon.

Question 5

Combiners Increase the efficiency of a MapReduce program because:

A. They provide a mechanism for different mappers to communicate with each Other, thereby reducing synchronization overhead.
B. They aggregate intermediate map output locally on each individual machine and therefore reduce the amount of data that needs to be shuffled across the network to the reducers.
C. They provide an optimization and reduce the total number of computations that are needed to execute an algorithm by a factor of n, where is the number of reducer.
D. They aggregate intermediate map output horn a small number of nearby (i.e., rack-local) machines and therefore reduce the amount of data that needs to be shuffled across the network to the reducers.

Solutions: