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Hortonworks Hadoop 2.0 Certification exam for Pig and Hive Developer Sample Questions:

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

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

2. Which HDFS command copies an HDFS file named foo to the local filesystem as localFoo?

A) hadoop fs -put foo LocalFoo
B) hadoop fs -Is foo
C) hadoop -cp foo LocalFoo
D) hadoop fs -get foo LocalFoo

3. You write MapReduce job to process 100 files in HDFS. Your MapReduce algorithm uses TextInputFormat: the mapper applies a regular expression over input values and emits key-values pairs with the key consisting of the matching text, and the value containing the filename and byte offset. Determine the difference between setting the number of reduces to one and settings the number of reducers to zero.

A) With zero reducers, no reducer runs and the job throws an exception. With one reducer, instances of matching patterns are stored in a single file on HDFS.
B) With zero reducers, all instances of matching patterns are gathered together in one file on HDFS. With one reducer, instances of matching patterns are stored in multiple files on HDFS.
C) With zero reducers, instances of matching patterns are stored in multiple files on HDFS. With one reducer, all instances of matching patterns are gathered together in one file on HDFS.
D) There is no difference in output between the two settings.

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

5. Can you use MapReduce to perform a relational join on two large tables sharing a key? Assume that the two tables are formatted as comma-separated files in HDFS.

A) Yes.
B) Yes, so long as both tables fit into memory.
C) No, but it can be done with either Pig or Hive.
D) No, MapReduce cannot perform relational operations.
E) Yes, but only if one of the tables fits into memory

Solutions: