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

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

3. Which of the following describes how a client reads a file from HDFS?

A) The client contacts the NameNode for the block location(s). The NameNode then queries the DataNodes for block locations. The DataNodes respond to the NameNode, and the NameNode redirects the client to the DataNode that holds the requested data block(s). The client then reads the data directly off the DataNode.
B) The client queries the NameNode for the block location(s). The NameNode returns the block location(s) to the client. The client reads the data directly off the DataNode(s).
C) The client contacts the NameNode for the block location(s). The NameNode contacts theDataNode that holds the requested data block. Data is transferred from the DataNode to the NameNode, and then from the NameNode to the client.
D) The client queries all DataNodes in parallel. The DataNode that contains the requested data responds directly to the client. The client reads the data directly off the DataNode.

4. If you run the word count MapReduce program with m mappers and r reducers, how many output files will you get at the end of the job? And how many key-value pairs will there be in each file? Assume k is the number of unique words in the input files.

A) There will be m files, each with approximately k/m key-value pairs.
B) There will be r files, each with exactly k/r key-value pairs.
C) There will be r files, each with approximately k/r key-value pairs.
D) There will be r files, each with approximately k/m key-value pairs.
E) There will be m files, each with exactly k/m key value pairs.

5. Which of the following utilities allows you to create and run MapReduce jobs with any executable or script as the mapper and/or the reducer?

A) Hadoop Streaming
B) Oozie
C) Sqoop
D) Flume

Solutions: