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Cloudera Certified Developer for Apache Hadoop Sample Questions:

1. In a MapReduce job, the reducer receives all values associated with the same key. Which statement is most accurate about the ordering of these values?

A) The values are arbitrarily ordered, but multiple runs of the same MapReduce job will always have the same ordering.
B) Since the values come from mapper outputs, the reducers will receive contiguous sections of sorted values.
C) The values are in sorted order.
D) The values are arbitrarily ordered, and the ordering may vary from run to run of the same MapReduce job.

2. MapReduce is well-suited for all of the following applications EXCEPT? (Choose one):

A) Analysis of large amounts of Web logs (queries, clicks, etc.).
B) Online transaction processing (OLTP) for an e-commerce Website.
C) Graph mining on a large social network (e.g., Facebook friends network).
D) Text mining on a large collections of unstructured documents.

3. What is a SequenceFile?

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

4. What is the preferred way to pass a small number of configuration parameters to a mapper or reducer?

A) As key-value pairs in the jobconf object.
B) Through a static variable in the MapReduce driver class (i.e., the class that submits the MapReduce job).
C) Using a plain text file via the Distributedcache, which each mapper or reducer reads.
D) As a custom input key-value pair passed to each mapper or reducer.

5. In a MapReduce job, you want each of you input files processed by a single map task. How do you configure a MapReduce job so that a single map task processes each input file regardless of how many blocks the input file occupies?

A) Increase the parameter that controls minimum split size in the job configuration.
B) Set the number of mappers equal to the number of input files you want to process.
C) Write a custom FileInputFormat and override the method isSplittable to always return false.
D) Write a custom MapRunner that iterates over all key-value pairs in the entire file.

Solutions: