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 that finish first are used. This is called:

In a MapReduce job with 500 map tasks, how many map task attempts will there be?

You have written a Mapper which invokes the following five calls to the outputcollector.collect
method:
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How many times will the Reducer’s reduce method be invoked.

You need to move a file titled “weblogs” into HDFS. When you try to copy the file, you can’t. You
know you have ample space on your DataNodes. Which action should you take to relieve this
situation and store more files in HDFS?

In the reducer, the MapReduce API provides you with an iterator over Writable values. What does
calling the next () method return?

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

Your client application submits a MapReduce job to your Hadoop cluster. The Hadoop framework
looks for an available slot to schedule the MapReduce operations on which of the following
Hadoop computing daemons?

Which of the Following best describes the lifecycle of a Mapper?

Analyze each scenario below and indentify which best describes the behavior of the default
partitioner?

How does the NameNode detect that a DataNode has failed?