Mapr (HP) Hadoop Developer Certification Questions and Answers (Dumps and Practice Questions)

Question : The Hadoop API uses basic Java types such as LongWritable, Text, IntWritable. They have almost the same features as default java classes.
What are these writable data types optimized for?



1. Writable data types are specifically optimized for network transmissions
2. Writable data types are specifically optimized for file system storage
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4. Writable data types are specifically optimized for data retrieval



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Explanation: Data needs to be represented in a format optimized for network transmission. Hadoop is based on the ability to send data between datanodes very quickly. Writable data types
are used for this purpose.





Question : Can a custom type for data Map-Reduce processing be implemented?


1. No, Hadoop does not provide techniques for custom datatypes
2. Yes, but only for mappers
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4. Yes, but only for reducers

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Developers can easily implement new data types for any objects. It is common practice to use existing classes and extend them with writable interface.




Question : What happens if mapper output does not match reducer input?


1. Hadoop API will convert the data to the type that is needed by the reducer.
2. Data input/output inconsistency cannot occur. A preliminary validation check is executed prior
to the full execution of the job to ensure there is consistency.
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4. A real-time exception will be thrown and map-reduce job will fail



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Explanation: Reducers are based on the mappers output and Java is a strongly typed language. Therefore, an exception will be thrown at run-time if types do not much.

Related Questions

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Question : You have following sample Mapper class and its map() method.

public class ProjectionMapper extends Mapper {
private Text word = new Text();
private LongWritable count = new LongWritable();
@Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String[] split = value.toString().split("\t+");
word.set(split[0]);
if (split.length > 2) {
try {
count.set(Long.parseLong(split[2]));
context.write(word, count);
} catch (NumberFormatException e) {
}
}
}
}
Now, select the correct statement based on above code.

1. Four arguments to the Mapper Class added in angle bracket are for Input key and value as well output key and value

2. Mapper class always use map() method

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4. 1,2

5. 1,2,3


Question : Select correct statement regarding map() method



1. each call to map() method will produce List of key value pair for all the records in input split

2. each call to map() method will produce List of key value pair for all the records in a block of a file

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4. Both 1 and 2



Question : We have reduce class example as below.

public class LongSumReducer extends Reducer {

private LongWritable result = new LongWritable();

public void reduce(KEY key, Iterable values,
Context context) throws IOException, InterruptedException {
long sum = 0;
for (LongWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}

Select correct option

1. reduce methods emits final results as key and value. Both will be saved on hdfs.

2. reduce methods emits final results value and will be saved on hdfs.

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4. 1,3

5. 2,3


Question : If the output key of the Mapper is Text than Input key of the reducer must be Text

1. True
2. False


Question : Which of is the correct way, by which hadoop job can be launched?



1. synchronously

2. asynchronously

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4. None of 1 and 2



Question : When you write a Java MapReduce application, which method will be the entry point for application

1. main()

2. ToolRunner.run()

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4. reduce()

5. job.waitForCompletion()