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Cloudera Certified Developer for Apache Hadoop Sample Questions:

1. You've written a MapReduce job that will process 500 million input records and generate 500 million key-value pairs. The data is not uniformly distributed. Your MapReduce job will create a significant amount of intermediate data that it needs to transfer between mappers and reducers which is a potential bottleneck. A custom implementation of which of the following interfaces is most likely to reduce the amount of intermediate data transferred across the network?

A) Combiner
B) Partitioner
C) OutputFormat
D) InputFormat
E) Writable
F) WritableComparable

2. In the reducer, the MapReduce API provides you with an iterator over Writable values. Calling the next () method:

A) Returns a reference to the same writable object each time, but populated with different data.
B) Returns a reference to a Writable object. The API leaves unspecified whether this is a reused object or a new object.
C) Returns a reference to a Writable object from an object pool.
D) Returns a reference to the same writable object if the next value is the same as the previous value, or a new writable object otherwise.
E) Returns a reference to a different Writable object each time.

3. You have the following key value pairs as output from your Map task:
(The, 1)
(Fox, 1)
(Runs, 1)
(Faster, 1)
(Than, 1)
(The, 1)
(Dog, 1)
How many keys will be passed to the reducer?

A) Five
B) Six
C) Four
D) One
E) Three
F) Two

4. What happens in a MapReduce job when you set the number of reducers to one?

A) Setting the number of reducers to one is invalid, and an exception is thrown.
B) A single reducer gathers and processes all the output from all the mappers. The output is written to a single file in HDFS.
C) Setting the number of reducers to one creates a processing bottleneck, and since the number of reducers as specified by the programmer is used as a reference value only, the MapReduce runtime provides a default setting for the number of reducers.
D) A single reducer gathers and processes all the output from all the mappers. The output is written in as many separate files as there are mappers.

5. Which happens if the NameNode crashes?

A) HDFS becomes unavailable to new MapReduce jobs, but running jobs will continue until completion.
B) HDFS becomes temporarily unavailable until an administrator starts redirecting client requests to the Secondary NameNode.
C) HDFS becomes unavailable until the NameNode is restored.
D) The Secondary NameNode seamlessly takes over and there is no service interruption.

Solutions: