Practice Test -Hadoop

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1. What is distributed cache?

The distributed cache is special component on namenode that will cache frequently used data for faster client response. It is used during reduce step.
The distributed cache is special component on datanode that will cache frequently used data for faster client response. It is used during map step
The distributed cache is a component that caches java objects.
The distributed cache is a component that allows developers to deploy jars for Map-Reduce processing.

2. You want to count the number of occurrences for each unique word in the supplied input data.You’ve decided to implement this by having your mapper tokenize each word and emit a literal value 1, and then have your reducer increment a counter for each literal 1 it receives. After successful implementing this, it occurs to you that you could optimize this by specifying a combiner. Will you be able to reuse your existing Reduces as your combiner in this case and why or why not?

Yes, because the sum operation is both associative and commutative and the input and output types to the reduce method match.
No, because the sum operation in the reducer is incompatible with the operation of a Combiner.

No, because the Reducer and Combiner are separate interfaces.

No, because the Combiner is incompatible with a mapper which doesn’t use the same data type for both the key and value.

3. Which of the following MapReduce execution frameworks focus on execution in sharedmemory environments?

Hadoop
Twister
Phoenix
C

4. Can you provide multiple input paths to a map-reduce jobs?

Yes, but only in Hadoop 0.22+.
No, Hadoop always operates on one input directory.
Yes, developers can add any number of input paths.
Yes, but the limit is currently capped at 10 input paths.

5. In a MapReduce job, the reducer receives all values associated with same key. Which statement best describes the ordering of these values?

The values are in sorted order.
The values are arbitrarily ordered, and the ordering may vary from run to run of the same MapReduce job.
The values are arbitrary ordered, but multiple runs of the same MapReduce job will always have the same ordering.
Since the values come from mapper outputs, the reducers will receive contiguous sections of sorted values.

6. What is map - side join?

Map-side join is done in the map phase and done in memory
Map-side join is a technique in which data is eliminated at the map step
Map-side join is a form of map-reduce API which joins data from different locations

None of these answers are correct

7. What are map files and why are they important?

Map files are stored on the namenode and capture the metadata for all blocks on a particular rack.
Map files are the files that show how the data is distributed in the Hadoop cluster
Map files are generated by Map-Reduce after the reduce step. They show the task distribution during job execution

Map files are sorted sequence files that also have an index. The index allows fast data look up.

8. What is the input to the Reduce function?

One key and a list of all values associated with that key.
One key and a list of some values associated with that key
An arbitrarily sized list of key/value pairs.
.Distributed filesystems must always be resident in memory, which is much faster than disk.

9. Why would a developer create a map-reduce without the reduce step?

Developers should design Map-Reduce jobs without reducers only if no reduce slots are available on the cluster.

Developers should never design Map-Reduce jobs without reducers. An error will occur upon compile.

There is a CPU intensive step that occurs between the map and reduce steps. Disabling the reduce step speeds up data processing.
It is not possible to create a map-reduce job without at least one reduce step. A developer may decide to limit to one reducer for debugging purposes.

10. How can a distributed filesystem such as HDFS provide opportunities for optimization of a MapReduce operation?

Data represented in a distributed filesystem is already sorted.
Distributed filesystems must always be resident in memory, which is much faster than disk.
Data storage and processing can be co-located on the same node, so that most input data relevant to Map or Reduce will be present on local disks or cache.
A distributed filesystem makes random access faster because of the presence of a dedicated node serving file metadata.

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Hadoop