Question 1

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 key-value 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.

Accepted Answer

E

Question 2

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.

Accepted Answer

E

Question 3

Indentify which best defines a SequenceFile?
A) A SequenceFile contains a binary encoding of an arbitrary number of homogeneous Writable objects
B) A SequenceFile contains a binary encoding of an arbitrary number of heterogeneous Writable objects
C) A SequenceFile contains a binary encoding of an arbitrary number of WritableComparable objects, in sorted order.
D) A SequenceFile contains a binary encoding of an arbitrary number key-value pairs. Each key must be the same type. Each value must be the same type.

Accepted Answer

A SequenceFile is a flat file consisting of binary key-value pairs, where all keys are of one type and all values are of another type.

Question 4

Determine which best describes when the reduce method is first called in a MapReduce job?
A) Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed. The programmer can configure in the job what percentage of the intermediate data should arrive before the reduce method begins.
B) Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed. The reduce method is called only after all intermediate data has been copied and sorted.
C) Reduce methods and map methods all start at the beginning of a job, in order to provide optimal performance for map-only or reduce-only jobs.
D) Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed. The reduce method is called as soon as the intermediate key-value pairs start to arrive.

Accepted Answer

Reducers start copying intermediate key-value pairs from each Mapper as soon as it has completed. The reduce method is called as soon as the intermediate key-value pairs start to arrive.

Question 5

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?
A) HDFS command
B) Pig LOAD command
C) Sqoop import
D) Hive LOAD DATA command
E) Ingest with Flume agents
F) Ingest with Hadoop Streaming

Accepted Answer

The answer of You have user profile records in your...

Question 6

In a MapReduce job, you want each of your input files processed by a single map task. How do you configure a MapReduce job so that a single map task processes each input file regardless of how many blocks the input file occupies?
A) Increase the parameter that controls minimum split size in the job configuration.
B) Write a custom MapRunner that iterates over all key-value pairs in the entire file.
C) Set the number of mappers equal to the number of input files you want to process.
D) Write a custom FileInputFormat and override the method isSplitable to always return false.

Accepted Answer

By overriding the isSplitable method to return false, you ensure that each input file is treated as a single split, thus processed by a single map task.

Question 7

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.

Accepted Answer

The answer of Which best describes how TextInputFormat processes input...

Question 8

On a cluster running MapReduce v1 (MRv1), a TaskTracker heartbeats into the JobTracker on your cluster, and alerts the JobTracker it has an open map task slot. What determines how the JobTracker assigns each map task to a TaskTracker?
A) The amount of RAM installed on the TaskTracker node.
B) The amount of free disk space on the TaskTracker node.
C) The number and speed of CPU cores on the TaskTracker node.
D) The average system load on the TaskTracker node over the past fifteen (15) minutes.
E) The location of the InsputSplit to be processed in relation to the location of the node.

Accepted Answer

The JobTracker assigns map tasks to TaskTrackers based on data locality, meaning it tries to assign tasks to nodes where the data is already located to minimize data transfer and improve performance.

Question 9

Which process describes the lifecycle of a Mapper?
A) The JobTracker calls the TaskTracker's configure () method, then its map () method and finally its close () method.
B) The TaskTracker spawns a new Mapper to process all records in a single input split.
C) The TaskTracker spawns a new Mapper to process each key-value pair.
D) The JobTracker spawns a new Mapper to process all records in a single file.

Accepted Answer

The answer of Which process describes the lifecycle of a...

Question 10

The Hadoop framework provides a mechanism for coping with machine issues such as faulty configuration or impending hardware failure. MapReduce detects that one or a number of machines are performing poorly and starts more copies of a map or reduce task. All the tasks run simultaneously and the task finish first are used. This is called:
A) Combine
B) IdentityMapper
C) IdentityReducer
D) Default Partitioner
E) Speculative Execution

Accepted Answer

Speculative Execution is a feature in Hadoop's MapReduce that launches duplicate tasks to handle slow-running tasks, ensuring faster completion by using the results of the task that finishes first.

Question 11

What is the disadvantage of using multiple reducers with the default HashPartitioner and distributing 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.

Accepted Answer

Using multiple reducers with the default HashPartitioner can lead to output files that are not globally sorted because each reducer works on a separate partition of the data, and the default partitioning does not ensure global ordering across partitions.

Question 12

To process input key-value pairs, your mapper needs to lead a 512 MB data file in memory. What is the best way to accomplish this?
A) Serialize the data file, insert in it the JobConf object, and read the data into memory in the configure method of the mapper.
B) Place the data file in the DistributedCache and read the data into memory in the map method of the mapper.
C) Place the data file in the DataCache and read the data into memory in the configure method of the mapper.
D) Place the data file in the DistributedCache and read the data into memory in the configure method of the mapper.

Accepted Answer

The answer of To process input key-value pairs, your mapper...

Question 13

You have the following key-value pairs as output from your Map task: (the, 1) (fox, 1) (faster, 1) (than, 1) (dog, 1) How many keys will be passed to the Reducer's reduce method?
A) Six
B) Five
C) Four
D) Two
E) One
F) Three

Accepted Answer

There are six unique keys in the given key-value pairs: "the", "fox", "faster", "than", "dog", and "jumps". Therefore, six keys will be passed to the Reducer's reduce method.

Question 14

You need to perform statistical analysis in your MapReduce job and would like to call methods in the 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

Accepted Answer

Using the -libjars option when submitting the job allows you to include additional JAR files that are needed by your MapReduce job, ensuring they are available on all nodes at runtime.

Question 15

Identify the MapReduce v2 (MRv2 / YARN) daemon responsible for launching application containers and monitoring application resource usage?
A) ResourceManager
B) NodeManager
C) ApplicationMaster
D) ApplicationMasterService
E) TaskTracker
F) JobTracker

Accepted Answer

ApplicationMaster is responsible for launching application containers and monitoring application resource usage.

Question 16

You have written a Mapper which invokes the following five calls to the OutputColletor.collect method: output.collect (new Text ("Apple"), new Text ("Red") ) ; output.collect (new Text ("Banana"), new Text ("Yellow") ) ; output.collect (new Text ("Apple"), new Text ("Yellow") ) ; output.collect (new Text ("Cherry"), new Text ("Red") ) ; output.collect (new Text ("Apple"), new Text ("Green") ) ; How many times will the Reducer's reduce method be invoked?
A) 6
B) 3
C) 1
D) 0
E) 5

Accepted Answer

The Reducer's reduce method is invoked once per unique key. Here, the unique keys are "Apple", "Banana", and "Cherry", so the reduce method will be invoked 3 times.

Question 17

All keys used for intermediate output from mappers must:
A) Implement a splittable compression algorithm.
B) Be a subclass of FileInputFormat.
C) Implement WritableComparable.
D) Override isSplitable.
E) Implement a comparator for speedy sorting.

Accepted Answer

All keys used for intermediate output from mappers must implement WritableComparable to ensure they can be serialized and compared during the shuffle and sort phase in Hadoop.

Question 18

A client application creates an HDFS file named foo.txt with a replication factor of 3. Identify which best describes the file access rules in HDFS if the file has a single block that is stored on data nodes A, B and C?
A) The file will be marked as corrupted if data node B fails during the creation of the file.
B) Each data node locks the local file to prohibit concurrent readers and writers of the file.
C) Each data node stores a copy of the file in the local file system with the same name as the HDFS file.
D) The file can be accessed if at least one of the data nodes storing the file is available.

Accepted Answer

HDFS is designed to be fault-tolerant, and a file can be accessed as long as at least one replica is available on any data node.

Question 19

What data does a Reducer reduce method process?
A) All the data in a single input file.
B) All data produced by a single mapper.
C) All data for a given key, regardless of which mapper(s) produced it.
D) All data for a given value, regardless of which mapper(s) produced it.

Accepted Answer

The Reducer reduce method processes all data associated with a particular key, aggregating results from all mappers that produced data for that key.

Question 20

Given a directory of files with the following structure: line number, tab character, string: Example: 1    abialkjfjkaoasdfjksdlkjhqweroij 2    kadfjhuwqounahagtnbvaswslmnbfgy 3    kjfteiomndscxeqalkzhtopedkfsikj You want to send each line as one record to your Mapper. Which InputFormat should you use to complete the line: conf.setInputFormat (____.class) ; ?
A) SequenceFileAsTextInputFormat
B) SequenceFileInputFormat
C) KeyValueFileInputFormat
D) BDBInputFormat

Accepted Answer

The answer of Given a directory of files with the...

Exam 2: Cloudera Certified Developer for Apache Hadoop (CCDH)

For each intermediate key, each reducer task can emit:

For each input key-value pair, mappers can emit:

Indentify which best defines a SequenceFile?

Determine which best describes when the reduce method is first called in a MapReduce job?

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?

In a MapReduce job, you want each of your input files processed by a single map task. How do you configure a MapReduce job so that a single map task processes each input file regardless of how many blocks the input file occupies?

Which best describes how TextInputFormat processes input files and line breaks?

On a cluster running MapReduce v1 (MRv1) , a TaskTracker heartbeats into the JobTracker on your cluster, and alerts the JobTracker it has an open map task slot. What determines how the JobTracker assigns each map task to a TaskTracker?

Which process describes the lifecycle of a Mapper?

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

To process input key-value pairs, your mapper needs to lead a 512 MB data file in memory. What is the best way to accomplish this?

You have the following key-value pairs as output from your Map task: (the, 1) (fox, 1) (faster, 1) (than, 1) (dog, 1) How many keys will be passed to the Reducer's reduce method?

You need to perform statistical analysis in your MapReduce job and would like to call methods in the 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?

Identify the MapReduce v2 (MRv2 / YARN) daemon responsible for launching application containers and monitoring application resource usage?

All keys used for intermediate output from mappers must:

A client application creates an HDFS file named foo.txt with a replication factor of 3. Identify which best describes the file access rules in HDFS if the file has a single block that is stored on data nodes A, B and C?

What data does a Reducer reduce method process?