Hortonworks HDPCD Exam Questions Instant Access

Question 1

You are developing a combiner that takes as input Text keys, IntWritable values, and emits Text keys, IntWritable values. Which interface should your class implement?

ACombiner <Text, IntWritable, Text, IntWritable>

BMapper <Text, IntWritable, Text, IntWritable>

CReducer <Text, Text, IntWritable, IntWritable>

DReducer <Text, IntWritable, Text, IntWritable>

ECombiner <Text, Text, IntWritable, IntWritable>

Answer : D

Question 2

You have user profile records in your OLPT database, that you want to join with web logs you have already ingested into the Hadoop file system. How will you obtain these user records?

AHDFS command

BPig LOAD command

CSqoop import

DHive LOAD DATA command

EIngest with Flume agents

FIngest with Hadoop Streaming

Answer : C

Question 3

Indentify the utility that allows you to create and run MapReduce jobs with any executable or script as the mapper and/or the reducer?

AOozie

BSqoop

CFlume

DHadoop Streaming

Emapred

Answer : D

Hadoop streaming is a utility that comes with the Hadoop distribution. The utility allows you to create and run Map/Reduce jobs with any executable or script as the mapper and/or the reducer.

Question 4

Which one of the following Hive commands uses an HCatalog table named x?

ASELECT * FROM x;

BSELECT x.-FROM org.apache.hcatalog.hive.HCatLoader('x');

CSELECT * FROM org.apache.hcatalog.hive.HCatLoader('x');

DHive commands cannot reference an HCatalog table

Answer : C

Question 5

What is the disadvantage of using multiple reducers with the default HashPartitioner and distributing your workload across you cluster?

AYou will not be able to compress the intermediate data.

BYou will longer be able to take advantage of a Combiner.

CBy using multiple reducers with the default HashPartitioner, output files may not be in globally sorted order.

DThere are no concerns with this approach. It is always advisable to use multiple reduces.

Answer : C

Multiple reducers and total ordering

If your sort job runs with multiple reducers (either because mapreduce.job.reduces in mapred-site.xml has been set to a number larger than 1, or because you've used the -r option to specify the number of reducers on the command-line), then by default Hadoop will use the HashPartitioner to distribute records across the reducers. Use of the HashPartitioner means that you can't concatenate your output files to create a single sorted output file. To do this you'll need total ordering,

Question 6

Can you use MapReduce to perform a relational join on two large tables sharing a key? Assume that the two tables are formatted as comma-separated files in HDFS.

AYes.

BYes, but only if one of the tables fits into memory

CYes, so long as both tables fit into memory.

DNo, MapReduce cannot perform relational operations.

ENo, but it can be done with either Pig or Hive.

Answer : A

Note:

* Join Algorithms in MapReduce

A) Reduce-side join

B) Map-side join

C) In-memory join

/ Striped Striped variant variant

/ Memcached variant

* Which join to use?

/ In-memory join > map-side join > reduce-side join

/ Limitations of each?

In-memory join: memory

Map-side join: sort order and partitioning

Reduce-side join: general purpose

Question 7

What is the term for the process of moving map outputs to the reducers?

AReducing

BCombining

CPartitioning

DShuffling and sorting

Answer : D

Hortonworks Data Platform Certified Developer HDPCD Exam Questions