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Cloudera Certified Developer for Apache Hadoop Sample Questions:

1. Which happens if the NameNode crashes?

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

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

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

A) No reducer executes, but the mappers generate no output.
B) No reducer executes, but the outputs of all the mappers are gathered together and written to a single file in HDFS.
C) No reducer executes, and the output of each mapper is written to a separate file in HDFS.
D) Setting the number of reducers to zero is invalid, and an exception is thrown.

4. You have a large dataset of key-value pairs, where the keys are strings, and the values are integers. For each unique key, you want to identify the largest integer. In writing a MapReduce program to accomplish this, can you take advantage of a combiner?

A) Yes, but the number of unique keys must be known in advance.
B) Yes, as long as all the keys fit into memory on each node.
C) Yes, as long as all the integer values that share the same key fit into memory on each node.
D) No, a combiner would not be useful in this case.
E) Yes.

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

Solutions: