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QUESTION NO: 3

Which TWO of the following statements are true regarding Hive? Choose 2 answers

A. Useful for data analysts familiar with SQL who need to do ad-hoc queries

B. Offers real-time queries and row level updates

C. Allows you to define a structure for your unstructured Big Data

D. Is a relational database

Answer: A,C

QUESTION NO: 6

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.

Answer: C

Explanation: The MapReduce framework operates exclusively on pairs, that

is, the framework views the input to the job as a set of pairs and produces a

set of pairs as the output of the job, conceivably of different types.

The key and value classes have to be serializable by the framework and hence need to

implement the Writable interface. Additionally, the key classes have to implement the

WritableComparable interface to facilitate sorting by the framework.

Reference: MapReduce Tutorial

QUESTION NO: 4

You need to create a job that does frequency analysis on input data. You will do this by

writing a Mapper that uses TextInputFormat and splits each value (a line of text from an

input file) into individual characters. For each one of these characters, you will emit the

character as a key and an InputWritable as the value. As this will produce proportionally

more intermediate data than input data, which two resources should you expect to be

bottlenecks?

A. Processor and network I/O

B. Disk I/O and network I/O

C. Processor and RAM

D. Processor and disk I/O

Answer: B

QUESTION NO: 2

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?

A. Combiner

B. Mapper

2

C. Reducer

D. Reducer

E. Combiner

Answer: D

QUESTION NO: 8

How are keys and values presented and passed to the reducers during a standard sort and

5

shuffle phase 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.

Answer: A

Explanation: Reducer has 3 primary phases:

1. Shuffle

The Reducer copies the sorted output from each Mapper using HTTP across the network.

2. Sort

The framework merge sorts Reducer inputs by keys (since different Mappers may have

output the same key).

The shuffle and sort phases occur simultaneously i.e. while outputs are being fetched they

are merged.

SecondarySort

To achieve a secondary sort on the values returned by the value iterator, the application

should extend the key with the secondary key and define a grouping comparator. The keys

will be sorted using the entire key, but will be grouped using the grouping comparator to

decide which keys and values are sent in the same call to reduce.

3. Reduce

In this phase the reduce(Object, Iterable, Context) method is called for each <key,

(collection of values)> in the sorted inputs.

The output of the reduce task is typically written to a RecordWriter via

TaskInputOutputContext.write(Object, Object).

The output of the Reducer is not re-sorted.

6

Reference: org.apache.hadoop.mapreduce, Class

Reducer

QUESTION NO: 9

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.

Answer: A

Reference: How Map and Reduce operations are actually carried out

QUESTION NO: 10

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.

Answer: C

7

QUESTION NO: 1

Workflows expressed in Oozie can contain:

A. Sequences of MapReduce and Pig. These sequences can be combined with other

actions including forks, decision points, and path joins.

B. Sequences of MapReduce job only; on Pig on Hive tasks or jobs. These MapReduce

sequences can be combined with forks and path joins.

C. Sequences of MapReduce and Pig jobs. These are limited to linear sequences of

actions with exception handlers but no forks.

D. Iterntive repetition of MapReduce jobs until a desired answer or state is reached.

Answer: A

Explanation: Oozie workflow is a collection of actions (i.e. Hadoop Map/Reduce jobs, Pig

jobs) arranged in a control dependency DAG (Direct Acyclic Graph), specifying a sequence

of actions execution. This graph is specified in hPDL (a XML Process Definition Language).

hPDL is a fairly compact language, using a limited amount of flow control and action nodes.

Control nodes define the flow of execution and include beginning and end of a workflow

(start, end and fail nodes) and mechanisms to control the workflow execution path (

decision, fork and join nodes).

Workflow definitions

Currently running workflow instances, including instance states and variables

Reference: Introduction to Oozie

Note: Oozie is a Java Web-Application that runs in a Java servlet-container – Tomcat and

uses a database to store:

QUESTION NO: 5

Which one of the following classes would a Pig command use to store data in a table

defined in HCatalog?

3

A. org.apache.hcatalog.pig.HCatOutputFormat

B. org.apache.hcatalog.pig.HCatStorer

C. No special class is needed for a Pig script to store data in an HCatalog table

D. Pig scripts cannot use an HCatalog table

Answer: B

QUESTION NO: 7

What types of algorithms are difficult to express in MapReduce v1 (MRv1)?

A. Algorithms that require applying the same mathematical function to large numbers of

individual binary records.

B. Relational operations on large amounts of structured and semi-structured data.

C. Algorithms that require global, sharing states.

D. Large-scale graph algorithms that require one-step link traversal.

4

E. Text analysis algorithms on large collections of unstructured text (e.g, Web crawls).

Answer: C

Explanation: See 3) below.

Limitations of Mapreduce – where not to use Mapreduce

While very powerful and applicable to a wide variety of problems, MapReduce is not the

answer to every problem. Here are some problems I found where MapReudce is not suited

and some papers that address the limitations of MapReuce.

1. Computation depends on previously computed values

If the computation of a value depends on previously computed values, then MapReduce

cannot be used. One good example is the Fibonacci series where each value is summation

of the previous two values. i.e., f(k 2) = f(k 1) f(k). Also, if the data set is small enough to

be computed on a single machine, then it is better to do it as a single reduce(map(data))

operation rather than going through the entire map reduce process.

2. Full-text indexing or ad hoc searching

The index generated in the Map step is one dimensional, and the Reduce step must not

generate a large amount of data or there will be a serious performance degradation. For

example, CouchDB’s MapReduce may not be a good fit for full-text indexing or ad hoc

searching. This is a problem better suited for a tool such as Lucene.

3. Algorithms depend on shared global state

Solutions to many interesting problems in text processing do not require global

synchronization. As a result, they can be expressed naturally in MapReduce, since map

and reduce tasks run independently and in isolation. However, there are many examples of

algorithms that depend crucially on the existence of shared global state during processing,

making them difficult to implement in MapReduce (since the single opportunity for global

synchronization in MapReduce is the barrier between the map and reduce phases of

processing)

Reference: Limitations of Mapreduce – where not to use Mapreduce

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