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IIBM Institute of Business Management
IIBM Institute of Business Management
Examination Paper MM.100
Big Data
Section A: Objective Type & Short

Questions (30 Marks)
 This section consists of Multiple

choice and Short Note type questions
 Answer all the questions.
 Part One carries 1 mark each and Part

Two carries 5 marks each.
Part One:
Multiple choices:
1. What does commodity Hardware in Hadoop

world mean?
a. Very cheap hardware
b. Industry standard hardware
c. Discarded hardware
d. Low specifications Industry grade

hardware
2. Which of the following are NOT big

data problem(s)?
a. Parsing 5 MB XML file every 5 minutes
b. Processing IPL tweet sentiments
c. Processing online bank transactions
d. both (a) and (c)
3. What does “Velocity” in Big Data mean?
a. Speed of input data generation
b. Speed of individual machine processors
c. Speed of ONLY storing data
d. Speed of storing and processing data
4. The term Big Data first originated

from:
a. Stock Markets Domain
b. Banking and Finance Domain
c. Genomics and Astronomy Domain
d. Social Media Domain
5. Which of the following Batch

Processing instance is NOT an example of

Big Data Batch
Processing?
a. Processing 10 GB sales data every 6

hours
b. Processing flights sensor data
c. Web crawling app
d. Trending topic analysis of tweets for

last 15 minutesExamination Paper of

Business Analytics
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IIBM Institute of Business Management
6. Which of the following are example(s)

of Real Time Big Data Processing?
a. Complex Event Processing (CEP)

platforms
b. Stock market data analysis
c. Bank fraud transactions detection
d. both (a) and (c)
7. Sliding window operations typically

fall in the category

of__________________.
a. OLTP Transactions
b. Big Data Batch Processing
c. Big Data Real Time Processing
d. Small Batch Processing
8. What is HBase used as?
a. Tool for Random and Fast Read/Write

operations in Hadoop
b. Faster Read only query engine in

Hadoop
c. Map Reduce alternative in Hadoop
d. Fast Map Reduce layer in Hadoop
9. What is Hive used as?
a. Hadoop query engine
b. Map Reduce wrapper
c. Hadoop SQL interface
d. All of the above
10. Which of the following are NOT true

for Hadoop?
a. It’s a tool for Big Data analysis
b. It supports structured and

unstructured data analysis
c. It aims for vertical scaling out/in

scenarios
d. Both (a) and (c)
Part Two:
1. Define Unstructured Data Analytics.

Elaborate on Context-Sensitive and

Domain-Specific
Searches.
2. Define HDFS. Explain HDFS in detail.
3. What is Complexity Theory for Map-

Reduce? What is Reducer Size and

Replication Rate?
4. Write at least five Big Data Analytics

Applications in detail.
END OF SECTION AExamination Paper of

Business Analytics
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IIBM Institute of Business Management
Section B: Caselets (40 marks)
 This section consists of Caselets.
 Answer all the questions.
 Each caselet carries 20 marks.
 Detailed information should form the

part of your answer (Word limit 150 to

200 words).
Caselet 1
CloudEra
One major global financial services

conglomerate uses Cloudera and Datameer

to help identify rogue
trading activity. Teams within the firm’s

asset management group are performing ad

hoc analysis on daily
feeds of price, position, and order

information. Having ad hoc analysis to

all of the detailed data allows
the group to detect anomalies across

certain asset classes and identify

suspicious behavior. Users
previously relied solely on desktop

spreadsheet tools. Now, with Datameer and

Cloudera, users have a
powerful platform that allows them to

sift through more data more quickly and

avert potential losses
before they begin.
.A leading retail bank is using Cloudera

and Datameer to validate data accuracy

and quality as required by
the Dodd-Frank Act and other regulations.

Integrating loan and branch data as well

as wealth
management data, the bank’s data quality

initiative is responsible for ensuring

that every record is
accurate. The process includes subjecting

the data to over 50 data sanity and

quality checks. The results of
those checks are trended over time to

ensure that the tolerances for data

corruption and data domains
aren’t changing adversely and that the

risk profiles being reported to investors

and regulatory agencies are
prudent and in compliance with regulatory

requirements. The results are reported

through a data quality
dashboard to the Chief Risk Officer and

Chief Financial Officer, who are

ultimately responsible for
ensuring the accuracy of regulatory

compliance reporting as well as earnings

forecasts to investors
Questions:
1. What kind of data these companies

used. What was the size of the data? What

kind of of tools
technologies they used to process the

data?
2. What was the problem they were facing

and how the insight they got the data

helped them to
resolve the issue.
Caselet 2
Adopting a new technology is never a

trivial task. Introducing a brand new

tool into a data scientist’s
toolset is no different. The resistance

to change is especially high in companies

that employ tens or
hundreds of statisticians.

Understandably, analysts have learned to

love their tool and live with any
shortcomings. The effort required to

learn a more efficient tool often seems

too great even if such a
transition would lead to long-term time

savings. This is where Pivotal Data Labs

(PDL) comes into the
picture, using a team of highly skilled

set of data scientists and engineers to

prove results to our customers
such as:Examination Paper of Business

Analytics
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IIBM Institute of Business Management
 Shorter time to insight and to market
 Better utilization of all captured data

(both structured and unstructured)
 Improved model quality and better

decision-making
 Minimized data movement and need to

create multiple copies
Here describes an example journey to

technology adoption executed through a

series of data science
engagements solving real problems for our

customer, a major healthcare provider.

This customer has a
large division of research, and as a

trailblazer in preventive healthcare,

employs many accomplished
clinicians and biostatisticians who are

limited by the analytics tools that they

use. The journey they took
shows how analytics can be done faster

and better through a series of 5 projects

(Figure 1). Each project
answered different questions, proving the

need and utility of new tools in

advancing their data science
practices, improving their business, and

ultimately leading to the decision to

adopt new technology.
Questions:
1. What kind of pattern they identified

from the data & what kind of patterns

they were looking
from the data.
2. How they selected the tool/technology

to suit their need.
END OF SECTION B
Section C: Applied Theory (30 marks)
 This section consists of Long

Questions.
 Answer all the questions.
 Each question carries 15 marks.
 Detailed information should form the

part of your answer (Word limit 200 to

250 words).Examination Paper of Business

Analytics
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IIBM Institute of Business Management
1. Explain HBase and their data model and

implementations? Cassandra data model

with an
example? Explain in details about the

Hive data manipulation, queries, data

definition and
data types?
2. Explain Crowd sourcing analytics and

inter and Trans firewall analytics?
END OF SECTION C