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Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:

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

1. 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) Combine
C) Speculative Execution
D) IdentityReducer
E) Default Partitioner

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

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

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

4. Given the following Hive commands:

Which one of the following statements Is true?

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

5. To process input key-value pairs, your mapper needs to lead a 512 MB data file in memory. What is the best way to accomplish this?

A) Serialize the data file, insert in it the JobConf object, and read the data into memory in the configure method of the mapper.
B) Place the data file in the DistributedCache and read the data into memory in the map method of the mapper.
C) Place the data file in the DataCache and read the data into memory in the configure method of the mapper.
D) Place the data file in the DistributedCache and read the data into memory in the configure method of the mapper.

Solutions: