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

1. Given the following Hive commands:

Which one of the following statements Is true?

A) The file mydata.txt does not move from Its current location in HDFS
B) The file mydata.txt is moved to a subfolder of /apps/hive/warehouse
C) The file mydata.txt is copied to a subfolder of /apps/hive/warehouse
D) The file mydata.txt is copied into Hive's underlying relational database 0.

2. The Hadoop framework provides a mechanism for coping with machine issues such as faulty
configuration or impending hardware failure. MapReduce detects that one or a number of machines are
performing poorly and starts more copies of a map or reduce task. All the tasks run simultaneously and
the task finish first are used. This is called:

A) IdentityMapper
B) Speculative Execution
C) Combine
D) Default Partitioner
E) IdentityReducer

3. On a cluster running MapReduce v1 (MRv1), a TaskTracker heartbeats into the JobTracker on your
cluster, and alerts the JobTracker it has an open map task slot.
What determines how the JobTracker assigns each map task to a TaskTracker?

A) The amount of free disk space on the TaskTracker node.
B) The average system load on the TaskTracker node over the past fifteen (15) minutes.
C) The number and speed of CPU cores on the TaskTracker node.
D) The amount of RAM installed on the TaskTracker node.
E) The location of the InsputSplit to be processed in relation to the location of the node.

4. In a MapReduce job, the reducer receives all values associated with same key. Which statement best
describes the ordering of these values?

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

5. MapReduce v2 (MRv2/YARN) is designed to address which two issues?

A) Single point of failure in the NameNode.
B) Ability to run frameworks other than MapReduce, such as MPI.
C) HDFS latency.
D) Standardize on a single MapReduce API.
E) Resource pressure on the JobTracker.
F) Reduce complexity of the MapReduce APIs.

Solutions: