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

Question : Please map the below, for compression types?
A. NONE
B. RECORD
C. BLOCK

1. Do not compress
2. Compress only values
3. Access Mostly Uused Products by 50000+ Subscribers
4. Compress both value and key


1. A-1, B-4, C-3
2. A-1, B-3, C-4
3. Access Mostly Uused Products by 50000+ Subscribers
4. A-1, B-2, C-3
5. A-3, B-2, C-4

Correct Answer : Get Lastest Questions and Answer :
Explanation:




Question : Which of the following information, is stored in a header of Sequence file?
A. Magic number to know, it s a SequnceFile
B. Type of key
C. Type of Value
D. Compression Codec detail


1. A,D
2. C,D
3. Access Mostly Uused Products by 50000+ Subscribers
4. B,C,D
5. A,B,C,D

Correct Answer : Get Lastest Questions and Answer :
Explanation: However all of the above formats share a common header (which is used by the SequenceFile.Reader to return the appropriate
key/value pairs). The next section summarises the header:

SequenceFile Common Header

version - A byte array: 3 bytes of magic header 'SEQ', followed by 1 byte of actual version no. (e.g. SEQ4 or SEQ6)
keyClassName - String
valueClassName - String
compression - A boolean which specifies if compression is turned on for keys/values in this file.
blockCompression - A boolean which specifies if block compression is turned on for keys/values in this file.
compressor class - The classname of the CompressionCodec which is used to compress/decompress keys and/or values in this SequenceFile (if compression is
enabled).
metadata - SequenceFile.Metadata for this file (key/value pairs)
sync - A sync marker to denote end of the header.




Question : In case of SequenceFile, all the keys are stored in header and their respective values are stored as a content. Including key length and value
length.

1. True
2. False

Correct Answer : Get Lastest Questions and Answer :
Explanation: Both key and value are stored as a content of the file.

Related Questions

---

Question : When will you create Custom Combiner?



1. When you want to reduce the amount of network traffic during shuffle phase

2. If reducer is not a associative and commutative than , we should use Custom combiner else we could use Reducer as a Combiner

3. Access Mostly Uused Products by 50000+ Subscribers

4. 1,2

5. 1,2,3



Question : Select correct statement regarding combiner?



1. A combiner is called after all the Mappers finish and before the results are shuffled and transmitted over the network.

2. In Custom combiner output of the Combiner not necessarily match with the input of Reducer

3. Access Mostly Uused Products by 50000+ Subscribers

4. 1,2

5. 1,2,3



Question : If our Hadoop Job is CPU bound and Reducer is a Commutative and Associative. Then using reducer as a combiner will certainly improve the
performance.

1. True
2. False


Question : Select correct statement regarding combiner?



1. Use combiner, while doing aggregation and if number of keys is significantly less than the number of records after map phase.

2. Combiner can be called multiple times on Same Mapper Task node

3. Access Mostly Uused Products by 50000+ Subscribers

4. 1,2

5. 1,2,3



Question : Which is true regarding Combiner configuration and creation



1. Combiner can be set in Driver class

2. Combiner must implement combine() method

3. Access Mostly Uused Products by 50000+ Subscribers

4. 1,3

5. 1,2,3



Question : How can Map output compression, improves the performance of Hadoop Job ?



1. It reduces Disk I/O

2. It reduces CPU cycle

3. Access Mostly Uused Products by 50000+ Subscribers

4. It always uses ZLIB codec.