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Hortonworks HADOOP-PR000007 Exam Syllabus Topics:

Hortonworks-Certified-Apache-Hadoop-2.0-Developer(Pig and Hive Developer) Sample Questions:

1. You are developing a combiner that takes as input Text keys, IntWritable values, and emits Text keys,
IntWritable values. Which interface should your class implement?

A) Combiner <Text, IntWritable, Text, IntWritable>
B) Reducer <Text, Text, IntWritable, IntWritable>
C) Reducer <Text, IntWritable, Text, IntWritable>
D) Combiner <Text, Text, IntWritable, IntWritable>
E) Mapper <Text, IntWritable, Text, IntWritable>

2. A combiner reduces:

A) The number of values across different keys in the iterator supplied to a single reduce method call.
B) The number of input files a mapper must process.
C) The number of output files a reducer must produce.
D) The amount of intermediate data that must be transferred between the mapper and reducer.

3. You have just executed a MapReduce job. Where is intermediate data written to after being emitted from
the Mapper's map method?

A) Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker node
running the Reducer
B) Intermediate data in streamed across the network from Mapper to the Reduce and is never written to
disk.
C) Into in-memory buffers that spill over to the local file system of the TaskTracker node running the
Mapper.
D) Into in-memory buffers on the TaskTracker node running the Reducer that spill over and are written into
HDFS.
E) Into in-memory buffers on the TaskTracker node running the Mapper that spill over and are written into
HDFS.

4. Which Hadoop component is responsible for managing the distributed file system metadata?

A) Metanode
B) NameSpaceManager
C) NameNode
D) DataNode

5. What types of algorithms are difficult to express in MapReduce v1 (MRv1)?

A) Algorithms that require applying the same mathematical function to large numbers of individual binary
records.
B) Large-scale graph algorithms that require one-step link traversal.
C) Algorithms that require global, sharing states.
D) Text analysis algorithms on large collections of unstructured text (e.g, Web crawls).
E) Relational operations on large amounts of structured and semi-structured data.

Solutions: