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
7
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
8
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
9
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
10
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
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
7
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
8
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
9
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
10
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
No comments:
Post a Comment