Correct Answer: D

Explanation: The input types fed to the mapper are controlled by the InputFormat used. The default input

format, "TextInputFormat," will load data in as (LongWritable, Text) pairs. The long value is the byte offset

of the line in the file. The Text object holds the string contents of the line of the file.

Note: The data types emitted by the reducer are identified by setOutputKeyClass()

andsetOutputValueClass(). The data types emitted by the reducer are identified by setOutputKeyClass()

and setOutputValueClass().

By default, it is assumed that these are the output types of the mapper as well. If this is not the case, the

methods setMapOutputKeyClass() and setMapOutputValueClass() methods of the JobConf class will

override these.

Reference: Yahoo! Hadoop Tutorial, THE DRIVER METHOD

Correct Answer: A

Explanation: A MapReduce job with m mappers and r reducers involves up to m * r distinct copy operations, since each mapper may have intermediate output going to every reducer.

Correct Answer: D

Explanation: JobTracker is the daemon service for submitting and tracking MapReduce jobs in Hadoop. There is only One Job Tracker process run on any hadoop cluster. Job Tracker runs on its own JVM process. In a typical production cluster its run on a separate machine. Each slave node is configured with job tracker node location. The JobTracker is single point of failure for the Hadoop MapReduce service. If it goes down, all running jobs are halted. JobTracker in Hadoop performs following actions(from Hadoop Wiki:)

Client applications submit jobs to the Job tracker. The JobTracker talks to the NameNode to determine the location of the data The JobTracker locates TaskTracker nodes with available slots at or near the data The JobTracker submits the work to the chosen TaskTracker nodes. The TaskTracker nodes are monitored. If they do not submit heartbeat signals often enough, they are deemed to have failed and the work is scheduled on a different TaskTracker. A TaskTracker will notify the JobTracker when a task fails. The JobTracker decides what to do then: it may resubmit the job elsewhere, it may mark that specific record as something to avoid, and it may may even blacklist the TaskTracker as unreliable. When the work is completed, the JobTracker updates its status.

Client applications can poll the JobTracker for information.

Reference: 24 Interview Questions and Answers for Hadoop MapReduce developers, What is a JobTracker in Hadoop? How many instances of JobTracker run on a Hadoop Cluster?

Correct Answer: D

Explanation: HDFS keeps three copies of a block on three different datanodes to protect against true data corruption. HDFS also tries to distribute these three replicas on more than one rack to protect against data availability issues. The fact that HDFS actively monitors any failed datanode(s) and upon failure detection immediately schedules re-replication of blocks (if needed) implies that three copies of data on three different nodes is sufficient to avoid corrupted files. Note: HDFS is designed to reliably store very large files across machines in a large cluster. It stores each file as a sequence of blocks; all blocks in a file except the last block are the same size. The blocks of a file are replicated for fault tolerance. The block size and replication factor are configurable per file. An application can specify the number of replicas of a file. The replication factor can be specified at file creation time and can be changed later. Files in HDFS are write-once and have strictly one writer at any time. The NameNode makes all decisions regarding replication of blocks. HDFS uses rack-aware replica placement policy. In default configuration there are total 3 copies of a datablock on HDFS, 2 copies are stored on datanodes on same rack and 3rd copy on a different rack. Reference: 24 Interview Questions and Answers for Hadoop MapReduce developers , How the HDFS Blocks are replicated?

Correct Answer: C