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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 arbitrarily ordered, and the ordering may vary from run to run of the same MapReduce job.
D) The values are in sorted order.

2. You have an employee who is a Date Analyst and is very comfortable with SQL. He would like to run ad-hoc analysis on data in your HDFS duster. Which of the following is a data warehousing software built on top of Apache Hadoop that defines a simple SQL-like query language well-suited for this kind of user?

A) Hue
B) Pig
C) Hive
D) Flume
E) Sqoop
F) Hadoop Streaming
G) Oozie

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

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

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

5. The NameNode uses RAM for the following purpose:

A) To manage distributed read and write locks on files in HDFS.
B) To store filenames, list of blocks and other meta information.
C) To store the contents of files in HDFS.
D) To store the edits log that keeps track of changes in HDFS.

Solutions: