DW 2009-10 SEM2
Q.1. What are the different kinds of Metadata that exist in a data
warehouse? Explain with proper relevant examples. Compare the importance of
metadata in a data warehouse to OLTP systems. [5]
Q.2. Discuss the need of Data Warehousing in today’s business
environment. What are the main problems faced by Data Warehouse professionals
in real problem domains? [5]
Q.3. What, according to you, are the drawbacks of starting a data
warehouse project by building data marts? Also explain how Kimball overcame
these shortcomings. [5]
Q.4. Compare & contrast outriggers & mini-dimensions. Give
situations under which you would prefer an outrigger to a mini-dimension &
vice versa. [7]
Q.5. Explain the entire process of populating and refreshing
dimension and fact tables in a data warehouse. First identify the steps
involved in chronological order and then give detail of each step involved.
Also draw a flow-chart for the same. [10]
Q.6. Design star schema(s) to find out products which were on
promotion but did not sell. Give an SQL statement for the same. Give an
elaborate design of the promotion dimension and also discuss the granularity of
your schema(s) in detail. [8]
DW 2009-10 SEM1
Q.1. What are dependent and independent data marts? Describe their
roles in the overall data warehouse architecture. [8]
Q.2. Give situations under which there is a need to distort the
classical star schema. Some DW access tools require classical star schema. How
can we use such tools when the classical star schema is distorted? [8]
Q.3. What is a coverage fact table? What purpose it serves in a
grocery store sales data mart? Give a suitable schema of coverage fact table
and discuss its granularity. [8]
Q.4. What are the advantages and disadvantages of having finest
granularity data in the data warehouse and data marts? [8]
Q.5. What are the features and functionalities that a user should
expect from an OLAP tool? Give a brief description. [8]
DW 2008-09 SEM2
Q.1 What are the limitations of ER modeling when used for modeling
data in Data Warehouses? [5]
Q.2 What are the kinds of analysis that can be performed on Data
Warehouse data? [5]
Q.3 Design a data mart for storing and analyzing Alumni Data of
BITS. Give the analysis requirements first. Give example of some analysis
queries that can be answered using your schema. Also give some example tuples
for all the tables in your schema. And suggest partitioning and aggregation
strategies. Did the phenomenon of sparsity failure occur while creating
aggregates? [8]
Q.4 What are the advantages and disadvantages of having finest
granularity data in the data warehouse? [8]
Q.5 Explain why we go for normalization in designing operational
systems and why we avoid it in designing data warehousing systems. [7]
Q.6 Some information that is essential for an operational system
but is not required in a data warehouse system. Give examples. [7]
DW 2008-09 SEM1
Q.1 (a). Dimensional modeling is more restrictive than
Entity-Relationship modeling. Comment.
Q.1 (b). Several distortions of the classical star schema are
permissible. Describe them and the circumstances under which they are
justified. [4 + 4 = 8]
Q.2 Most of the analysis has a time component. Explain what role
time dimension plays in such analysis. Why it is recommended to have a separate
time dimension rather than having the date as one of the attributes in the fact
table? Where from does one get the time dimension? [8]
Q.3 Why it is recommended that the dimension tables in a star
schema be denormalized? What is the highest normal form of the dimension tables
and the fact tables? [8]
Q.4 What are inside-out and outside-in queries? What are
dimension-focused and factfocused queries? Give one example of each type of
query. [8]
Q.5 What are the strengths and weaknesses of the relational
technology when it comes to using it for data warehouses? [8]
DW 2007-08 2SEM
Q.1 How is a data warehouse different from a database? How are
they similar? [4]
Q.2 Suppose your task as a software engineer at BITS-University is
to design a data warehouse system to examine their university course database,
which contains the following information : the name, address, and status (e.g.,
undergraduate or graduate) of each student, the courses taken, and their cumulative
grade point average (CGPA).
Describe the architecture you would choose. What is the purpose of
each component of this architecture? [7]
Q.3 Draw a model for a database which can report on persons and
the apartments they reside in. A city has many streets and a street has many
houses. Each house can have one or more apartments, and apartments can contain
more than one person. Each person can reside in more than one apartment. [5]
Q.4 Why would you recommend dimensional modeling for modeling data
in a data warehouse? What advantages it has over ER modeling? [7]
Q.5 Explain, using suitable examples, all the advantages of using
surrogate keys in data warehouses. [7]
Q.6 Explain why we go for normalization in designing operational
systems and why we avoid it in designing data warehousing systems. [5]
Q.7 Some information that is essential for an operational system
but is not required in a data warehouse system. Give examples. [5]
DW 2007-08 1SEM
Q.1 Discuss in detail the role of look-up tables in dimensional
modeling. [8]
Q.2 Why it is recommended to have finest granularity data in the
data warehouse?
What are the advantages and disadvantages of doing so? [8]
Q.3 Explain why most data warehouse implementations are closer to
Kimball’s bottom-up approach? What other alternatives approaches are available?
[8]
Q.4 What are the advantages and disadvantages of using the
relational database technology in data warehousing? [8]
Q.5 A dimension can appear many times in a fact table. What kind
of problems this can cause? Discuss how such situations are handled in
dimensional modeling? Give an example. [8]
DW 2006-07 2SEM
Q.1. Why it is recommended that the data warehouse system should
be separate from the operational system of the organization? (6)
Q.2. What are coverage fact tables and what purpose do they serve?
In a grocery store sales data mart, give the schema of promotion coverage
table. Also discuss its granularity and compare it with that of the sales fact
table. (10)
Q.3. Compare and contrast between a mini-dimension and an
outrigger. (6)
Q.4. What is meant by conforming the dimensions? Why is this
important in a data warehouse? (6)
Q.5. How does a snowflake schema differ from a star schema? Name
two advantages and two disadvantages of the snowflake schema. (6)
Q.6. Name any five types of activities that are part of the ETL
process. Which of these are time consuming and why? (6)
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