1.
When is the earliest point at which the reduce method of a given Reducer can be called?
A.
As soon as at least one mapper has finished processing its input split.
B.
As soon as a mapper has emitted at least one record.
C.
Not until all mappers have finished processing all records.
D.
It depends on the InputFormat used for the job.
2.
Which describes how a client reads a file from HDFS?
A.
The client queries the NameNode for the block location(s). The NameNode returns the block
location(s) to the client. The client reads the data directory off the DataNode(s).
B.
The client queries all DataNodes in parallel. The DataNode that contains the requested data
responds directly to the client. The client reads the data directly off the DataNode.
C.
The client contacts the NameNode for the block location(s). The NameNode then queries the
DataNodes for block locations. The DataNodes respond to the NameNode, and the NameNode
redirects the client to the DataNode that holds the requested data block(s). The client then reads
the data directly off the DataNode.
D.
The client contacts the NameNode for the block location(s). The NameNode contacts the
DataNode that holds the requested data block. Data is transferred from the DataNode to the
NameNode, and then from the NameNode to the client.
3.
Indentify the utility that allows you to create and run MapReduce jobs with any executable or scriptas the mapper and/or the reducer?
4.
You are developing a combiner that takes as input Text keys, IntWritable values, and emits Textkeys, IntWritable values. Which interface should your class implement?
5.
How are keys and values presented and passed to the reducers during a standard sort and shufflephase of MapReduce?
A.
Keys are presented to reducer in sorted order; values for a given key are not sorted.
B.
Keys are presented to reducer in sorted order; values for a given key are sorted in ascending
order.
C.
Keys are presented to a reducer in random order; values for a given key are not sorted.
D.
Keys are presented to a reducer in random order; values for a given key are sorted in
ascending order.
6.
Assuming default settings, which best describes the order of data provided to a reducer’s reducemethod:
A.
The keys given to a reducer aren’t in a predictable order, but the values associated with those
keys always are.
B.
Both the keys and values passed to a reducer always appear in sorted order.
C.
Neither keys nor values are in any predictable order.
D.
The keys given to a reducer are in sorted order but the values associated with each key are in
no predictable order
7.
You wrote a map function that throws a runtime exception when it encounters a control characterin input data. The input supplied to your mapper contains twelve such characters totals, spreadacross five file splits. The first four file splits each have two control characters and the last split hasfour control characters.Identify the number of failed task attempts you can expect when you run the job withmapred.max.map.attempts set to 4:
A.
You will have forty-eight failed task attempts
B.
You will have seventeen failed task attempts
C.
You will have five failed task attempts
D.
You will have twelve failed task attempts
E.
You will have twenty failed task attempts
8.
You want to populate an associative array in order to perform a map-side join. You’ve decided toput this information in a text file, place that file into the DistributedCache and read it in yourMapper before any records are processed.Indentify which method in the Mapper you should use to implement code for reading the file andpopulating the associative array?
9.
You’ve written a MapReduce job that will process 500 million input records and generated 500million key-value pairs. The data is not uniformly distributed. Your MapReduce job will create asignificant amount of intermediate data that it needs to transfer between mappers and reduceswhich is a potential bottleneck. A custom implementation of which interface is most likely to reducethe amount of intermediate data transferred across the network?
10.
Can you use MapReduce to perform a relational join on two large tables sharing a key? Assumethat the two tables are formatted as comma-separated files in HDFS.
A.
B.
Yes, but only if one of the tables fits into memory
C.
Yes, so long as both tables fit into memory.
D.
No, MapReduce cannot perform relational operations.
E.
No, but it can be done with either Pig or Hive.
11.
You have just executed a MapReduce job. Where is intermediate data written to after beingemitted from the Mapper’s map method?
A.
Intermediate data in streamed across the network from Mapper to the Reduce and is never
written to disk.
B.
Into in-memory buffers on the TaskTracker node running the Mapper that spill over and are
written into HDFS.
C.
Into in-memory buffers that spill over to the local file system of the TaskTracker node running
the Mapper.
D.
Into in-memory buffers that spill over to the local file system (outside HDFS) of the TaskTracker
node running the Reducer
E.
Into in-memory buffers on the TaskTracker node running the Reducer that spill over and are
written into HDFS.
12.
You want to understand more about how users browse your public website, such as which pagesthey visit prior to placing an order. You have a farm of 200 web servers hosting your website. Howwill you gather this data for your analysis?
A.
Ingest the server web logs into HDFS using Flume.
B.
Write a MapReduce job, with the web servers for mappers, and the Hadoop cluster nodes for
reduces.
C.
Import all users’ clicks from your OLTP databases into Hadoop, using Sqoop.
D.
Channel these clickstreams inot Hadoop using Hadoop Streaming.
E.
Sample the weblogs from the web servers, copying them into Hadoop using curl.
13.
You need to run the same job many times with minor variations. Rather than hardcoding all jobconfiguration options in your drive code, you’ve decided to have your Driver subclassorg.apache.hadoop.conf.Configured and implement the org.apache.hadoop.util.Tool interface.Indentify which invocation correctly passes.mapred.job.name with a value of Example to Hadoop?
A.
Hadoop “mapred.job.name=Example” MyDriver input output
B.
Hadoop “mapred.job.name=Example” MyDriver input output
C.
Hadoop MyDrive –D mapred.job.name=Example input output
D.
Hadoop setproperty mapred.job.name=Example MyDriver input output
E.
Hadoop setproperty (“mapred.job.name=Example”) MyDriver input output
14.
MapReduce v2 (MRv2/YARN) is designed to address which two issues?
A.
Single point of failure in the NameNode.
B.
Resource pressure on the JobTracker.
C.
D.
Ability to run frameworks other than MapReduce, such as MPI.
E.
Reduce complexity of the MapReduce APIs.
F.
Standardize on a single MapReduce API.
15.
You are developing a MapReduce job for sales reporting. The mapper will process input keysrepresenting the year (IntWritable) and input values representing product indentifies (Text).Indentify what determines the data types used by the Mapper for a given job.
A.
The key and value types specified in the JobConf.setMapInputKeyClass and
JobConf.setMapInputValuesClass methods
B.
The data types specified in HADOOP_MAP_DATATYPES environment variable
C.
The mapper-specification.xml file submitted with the job determine the mapper’s input key and
value types.
D.
The InputFormat used by the job determines the mapper’s input key and value types.
16.
Identify the MapReduce v2 (MRv2 / YARN) daemon responsible for launching applicationcontainers and monitoring application resource usage?
17.
Which best describes how TextInputFormat processes input files and line breaks?
A.
Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader
of the split that contains the beginning of the broken line.
B.
Input file splits may cross line breaks. A line that crosses file splits is read by the
RecordReaders of both splits containing the broken line.
C.
The input file is split exactly at the line breaks, so each RecordReader will read a series of
complete lines.
D.
Input file splits may cross line breaks. A line that crosses file splits is ignored.
E.
Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader
of the split that contains the end of the broken line.
18.
For each input key-value pair, mappers can emit:
A.
As many intermediate key-value pairs as designed. There are no restrictions on the types of
those key-value pairs (i.e., they can be heterogeneous).
B.
As many intermediate key-value pairs as designed, but they cannot be of the same type as the
input key-value pair.
C.
One intermediate key-value pair, of a different type.
D.
One intermediate key-value pair, but of the same type.
E.
As many intermediate key-value pairs as designed, as long as all the keys have the same types
and all the values have the same type.
19.
You have the following key-value pairs as output from your Map task:(the, 1)(fox, 1)(faster, 1)(than, 1)(the, 1)(dog, 1)How many keys will be passed to the Reducer’s reduce method?
20.
You have user profile records in your OLPT database, that you want to join with web logs youhave already ingested into the Hadoop file system. How will you obtain these user records?
A.
B.
C.
D.
E.
F.
Ingest with Hadoop Streaming
21.
What is the disadvantage of using multiple reducers with the default HashPartitioner anddistributing your workload across you cluster?
A.
You will not be able to compress the intermediate data.
B.
You will longer be able to take advantage of a Combiner.
C.
By using multiple reducers with the default HashPartitioner, output files may not be in globally
sorted order.
D.
There are no concerns with this approach. It is always advisable to use multiple reduces.
22.
Given a directory of files with the following structure: line number, tab character, string:Example:1abialkjfjkaoasdfjksdlkjhqweroij2kadfjhuwqounahagtnbvaswslmnbfgy3kjfteiomndscxeqalkzhtopedkfsikjYou want to send each line as one record to your Mapper. Which InputFormat should you use tocomplete the line: conf.setInputFormat (____.class) ; ?
A.
SequenceFileAsTextInputFormat
B.
C.
D.
23.
You need to perform statistical analysis in your MapReduce job and would like to call methods inthe Apache Commons Math library, which is distributed as a 1.3 megabyte Java archive (JAR) file.Which is the best way to make this library available to your MapReducer job at runtime?
A.
Have your system administrator copy the JAR to all nodes in the cluster and set its location in
the HADOOP_CLASSPATH environment variable before you submit your job.
B.
Have your system administrator place the JAR file on a Web server accessible to all cluster
nodes and then set the HTTP_JAR_URL environment variable to its location.
C.
When submitting the job on the command line, specify the –libjars option followed by the JAR
file path.
D.
Package your code and the Apache Commands Math library into a zip file named JobJar.zip
24.
The Hadoop framework provides a mechanism for coping with machine issues such as faultyconfiguration or impending hardware failure. MapReduce detects that one or a number ofmachines are performing poorly and starts more copies of a map or reduce task. All the tasks runsimultaneously and the task finish first are used. This is called:
25.
For each intermediate key, each reducer task can emit:
A.
As many final key-value pairs as desired. There are no restrictions on the types of those keyvalue
pairs (i.e., they can be heterogeneous).
B.
As many final key-value pairs as desired, but they must have the same type as the intermediate
key-value pairs.
C.
As many final key-value pairs as desired, as long as all the keys have the same type and all the
values have the same type.
D.
One final key-value pair per value associated with the key; no restrictions on the type.
E.
One final key-value pair per key; no restrictions on the type.
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