CS614 Midterm Current Papers VU Fall 28Nov 2011

CS614 Midterm Current Papers VU Fall 2011

CS614 Midterm Current Papers VU Fall 2011

It is sometimes more efficient for an application to draw directly in a window without relying on the WM_PAINT message. How this task can be accomplished (i.e. how can we draw in a window directly without using WM_PAINT message)? (5)

Write down a C/C++ program that has 2 functions. One takes four integer variables as parameters and returns their sum and the other also takes 4 integers as argument and returns their multiplication. Also write 2 macros that perform the same tasks as these functions perform.(5)

How can I use the CopyTo method of the Windows Forms controls collection to copy controls into an array? (3)

How Windows keep track of the files?(3)

Can you write a class without specifying namespace? Which namespace does it belong to by default?(2)

"In the GDI environment there are two working spaces", Name these two. (2)

Another Paper:

MIDTERM EXAMINATION

Spring 2011

CS614- Data Warehousing (Session - 6)

Ref No: 1368137

Time: 60 min

Marks: 40

Question No: 1 ( Marks: 1 ) - Please choose one

The need to synchronize data upon update is called

Data Manipulation

Data Replication

Data Coherency

Data Imitation

Question No: 2 ( Marks: 1 ) - Please choose one

Taken jointly, the extract programs or naturally evolving systems formed a spider web,

also known as

Distributed Systems Architecture

Legacy Systems Architecture

Online Systems Architecture

Intranet Systems Architecture

Question No: 3 ( Marks: 1 ) - Please choose one

For good decision making, data should be integrated across the organization to cross the

LoB (Line of Business). This is to give the total view of organization from:

Owner's Perspective

Customer's Perspective

Decision Maker's Perspective

Employee's Perspective

Question No: 4 ( Marks: 1 ) - Please choose one

Node of a B-Tree is stored in memory block and traversing a B-Tree involves ______

page faults.

O (n)

O (n2)

O (n lg n)

O (lg n)

Question No: 5 ( Marks: 1 ) - Please choose one

Which statement is true for De-Normalization?

Redundant data is a performance liability at query time, but is a performance

benefit at update time.

Redundant data is a performance benefit at both query time and update time.

Redundant data is a performance liability at both query time and update time.

Redundant data is a performance benefit at query time, but is a performance

liability at update time.

Question No: 6 ( Marks: 1 ) - Please choose one

Pre-join technique is used to avoid

Run time join

Compile time join

Load time join

Question No: 7 ( Marks: 1 ) - Please choose one

Cube is a __________ entity containing values of a certain fact at a certain aggregation

level at an intersection of a combination of dimensions.

Logical

Physical

Analytical

None of these

Question No: 8 ( Marks: 1 ) - Please choose one

The goal of star schema design is to simplify ________

Logical data model

Physical data model

Conceptual data model

None of these

Question No: 9 ( Marks: 1 ) - Please choose one

Grain is the ________ level of data stored in the warehouse

Atomic

Summarized

Aggregated

Cube

Question No: 10 ( Marks: 1 ) - Please choose one

Transactional fact tables do not have records for events that do not occur. These are

called

Not Recording Facts

Fact-less Facts

Null Facts

None of these

Question No: 11 ( Marks: 1 ) - Please choose one

A ________ dimension is a collection of random transactional codes, flags and/text

attributes that are unrelated to any particular dimension. The ______ dimension is simply

a structure that provides a convenient place to store the ______ attributes.

Junk

Time

Parallel

None of these

Question No: 12 ( Marks: 1 ) - Please choose one

During ETL process of an organization, suppose you have data which can be

transformed using any of the transformation method. Which of the following strategy will

be your choice for least complexity?

One-to-One Scalar Transformation ( but not sure )

One-to-Many Element Transformation

Many-to-Many Element Transformation

Many-to-One Element Transformation

Question No: 13 ( Marks: 1 ) - Please choose one

Change Data Capture is one of the challenging technical issues in _____________

Data Extraction

Data Loading

Data Transformation

Data Cleansing

Question No: 14 ( Marks: 1 ) - Please choose one

Rearranging the grouping of source data, delivering it to the destination database, and

ensuring the quality of data are crucial to the process of loading the data warehouse. Data

____________ is vitally important to the overall health of a warehouse project.

1. Cleansing

2. Cleaning

3. Scrubbing

Which of the following options is true?

Option 1 only

Option 2 only

Option 1 & 2 only

Option 1, 2 & 3

Question No: 15 ( Marks: 1 ) - Please choose one

When performing objective assessments, companies follow a set of principles to develop

metrics specific to their needs, there is hard to have "one size fits all" approach. Which of

the following statement represents the pervasive functional forms?

Simple Ratio, Min or Max Operation, Weighted Average

Only Complex Ratio, Min Operation, Max Operation

Only Simple Ratio, Min or Max Operation

Only Min or Max Operation, Weighted Average

Question No: 16 ( Marks: 1 ) - Please choose one

The input to the data warehouse can come from OLTP or transactional system but not

from other third party database.

True

False

Question No: 17 ( Marks: 1 ) - Please choose one

Normalization effects performance

True ( but not sure )

False

Question No: 18 ( Marks: 1 ) - Please choose one

Collapsing tables can be done on the ___________ relationships

One-to-One

Many-to-Many

Both One-to-One and Many-to-Many

None of these

Question No: 19 ( Marks: 1 ) - Please choose one

_________ breaks a table into multiple tables based upon common column values.

Horizontal splitting

Vertical splitting

Question No: 20 ( Marks: 1 ) - Please choose one

If w is the window size and n is the size of data set, then the complexity of merging

phase in BSN method is___________

O (n)

O (w)

O (w n)

O (w log n)

Question No: 21 ( Marks: 2 )

Briefly describe snowflake schema.

Ans:

Snowflake Schema: snowflaking is a method of normalizing the dimension tables in

star schema. When we completely normalize all the dimension tables, then the

resultent structure resemble a snowflakewith the fact table in the middle.

\

Snowflake Schema: Sometimes a pure star schema might suffer performance problems.

This can occur when a de-normalized dimension table becomes very large and penalizes

the star join operation. Conversely, sometimes a small outer-level dimension table does

not incur a significant join cost because it can be permanently stored in a memory buffer.

Furthermore, because a star structure exists at the center of a snowflake, an efficient star

join can be used to satisfy part of a query. Finally, some queries will not access data from

outer-level dimension tables. These queries effectively execute against a star schema that

contains smaller dimension tables. Therefore, under some circumstances, a snowflake

schema is more efficient than a star schema.

Question No: 22 ( Marks: 2 )

Why both aggregation and summarization are required?

Although summarization and aggregation are

sometimes used interchangeably

Summarization and aggregationare typically used for the following reasons:

They are required when the lowest level of detail stored in the data warehouse is

at a higher level than the detail arriving from the source. This situation occurs

when data warehouse queries do not require the lowest level of detail or

sometimes when sufficient disk space is not available to store all the data for the

time frame required by the data warehouse.

• They can be used to populate data marts from the data warehouse where the data

mart does not require the same level of detail as is stored in the warehouse.

• They can be used to roll up detail values when the detail is removed from the

warehouse because it is

Question No: 23 ( Marks: 3 )

Under what condition smart tools work properly to construct a less detailed aggregate

from more detailed aggregate?

Ans:

Smart tools will allow less detailed aggregates to be constructed from more detailed

aggregates (full aggregate awareness) at run-time so that we do not go all the way down

to the detail for every aggregation. However, for this to work, the metrics must be

additive (e.g., no ratios, averages, etc.). More detailed pre-aggregates are larger, but can

also be used to build less detailed aggregates on-the-go.

Question No: 24 ( Marks: 3 )

What is web scrapping? Give some of its uses.

Web scrapping is a process of applying screen scrapping techniques to the web. There

are several web scrapping products in the market and target business users who want to

creatively use the data, not write complex scripts. Some of the uses of scrapping are:

Building contact lists

Extracting product catalogs

Aggregating real-estate info

Automating search Ad listings

Clipping news articles etc.

Question No: 25 ( Marks: 5 )

Af

types of data loading strategies are and when each type of strategy is adopted? Explain.

Significance of Data Loading Strategies

Need to look at:

Data freshness

System performance

Data volatility

Data Freshness

Very fresh low update efficiency

Historical data, high update efficiency

Always trade-offs in the light of goals

System performance

Availability of staging table space

Impact on query workload

Data Volatility

Ratio of new to historical data

High percentages of data change (batch update)

Question No: 26 ( Marks: 5 )

What are the drawbacks of MOLAP? Also explain the curse of Dimensionality?

Drawbacks of MOLAP: