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CS614 SOLVED MCQs

Onlline Extraction is a kind of———————————data extraction.

• Logical
• Dimensional
• Physical page 132
• Multi valued

The —————- saw the advent of disk storage, or DASD( direct Access Storage Device) :

• 1960s

•    1970s page 13

• 1950s
• 1990s

In context of data warehouse, normally it becomes difficult to extract data from different sources because these sources are normally.

•    Heterogeneous       page 140

• Homogeneous
• Centralized
• Baseline

Which of the following is not a task of Data Transformation?

• Conversion
• Summarization
• Enrichment

•    Full Data Refresh  page 135

Which of the following is not an Orr’s Law of Data Quality”?

• “Data that is not used cannot be corrected!”
• “Data quality is a function of its use, not its collection!”
• “Data will be no better than its most stringent use!”

•    “Data duplication can be harmful for the organization! ”      page 181

Flat files are one of the prevalent structures used in ——————- data extraction:

• Online

•    Offline            page 134

• Incremental
• Full

Which of the following is NOT one of the advantages of changed data capture (CDC) technique?

• Flat files are not required

•    Limited query interface is required for data extraction           page 152

• No incremental on-line I/O required for log tape
• Extraction of changed data occurs immediately

The most common range partitioning is on

• Color

•    Date         page 66

• PhoneNo
• Name

A relation is said to be in first normal form(1NF), if it does not contain ________

• Single value column

•    Multi-valued column      page 43

• Derived column
• Composite column

In a fully normalized database, too many ____________are required

• Values

•    Joins                 page 49

• Queries
• Conditions

In the data warehouse, data is collection from ——————– sources:

• Homogeneous

•    Heterogeneous                  page 21

• External
• Internal

De-normalization is more like a “controlled crash” with the aim to ———— without loss of information:

• Check
• Balance
• Decrease

•    Enhance    page 49

—————– is making all efforts to increase effectiveness and efficiency in meeting and accepted customer expectation:

• Quality assurance

•   Quality improvement   page 183

• Quality maintenance
• Quality Establishment

————- is the application of intelligence and experience to get common goals.

•   Wisdom        page 11

• Education
• Power
• Information

In the data transformation, ———- is the rearrangement and simplification of individual

• Aggregation
• Enrichment  page 136
• Splitting joining
• Conversion

Grain of a fact table means :

•   The meaning of one fact table row   page 109

• The meaning of one dimensional table row
• Summary of aggregates in all fact tables
• Summary of aggregates in all dimension tables

Normalization —————– :

•   Reduces redundancy          page 41

• Increases redundancy
• Reduces joins
• Reduces tables

Which of the following is not an example of a typical grain :

• Individual transaction
• Daily aggregates
• Monthly aggregates

•    Normalized attributes    page 111

Multi-dimensional databases(MDDs) typically use ——————– formats to store pre-summarized cube structures:

• SQL

•    Proprietary file          page 79

• Object oriented
• Non-proprietary file

———— provides a combination of “relational databases access” and “cube” data structures within a single framework:

•    HOLAP                       page 78

• DOLAP
• MOLAP
• ROLAP

Data Warehouse provides the best support for analysis while OLAP carries out the ————-

task:

• Mandatory
• Whole

•    Analysis                                page 69

• Prediction

—————— involves splitting a table by columns so that a group of columns is placed into the new table and  the remaining columns are placed in another new table:

•    Vertical splitting             page 56

• Horizontal splitting
• Adding redundant column
• None of the given option

OLAP implementations are highly/completely —————— :

• Normalized

•    Demoralized             page 69

• Predictive
• Additive

If each cell of Relation R contains a single value ( no repeating values) then it is confirmed that :

•    Relation R is in 1^st Normal Form       page 43

• Relation R is in 2^nd Normal Form
  • Relation R is in 3^rd Normal Form
  • Relation R is in 3^rd Normal Form but not in 2^nd Normal Form

Which kind of relationships is captured by Fact less fact table:

•    Many- to- Many                  page 121

• One-to-many
• One-to-one
• None of the given option

Which of the following is not an example of dimension:

• Product
• Date
• Region

•    Sales volume                        page 78

Which people criticize Dimensional Modeling (DM) as being a data mart oriented approach?

• Those that consider ER models as Data marts

•    Those that consider Business processes as Data marts  page 110

• Those that consider Data marts as Data warehouse
• Those that consider dimensional model
• Those that consider dimensional modeling as de-normalization approach

In a fully normalized form:

•    To many joins are required         page 49

• Relationships lose their significance
• No joins are required
• Data integrity becomes an issue

Which of the following is an example of Non-Additive Facts:

• Quality sold
• Total sale in Rs.

•    Discount in percentage                        page 119

• Count of orders in a store

Which of the following is not a CUBE operation?

•    ANSI SQL       page 81

• Roll Up
• Drill Down
• Pivoting

——————– allows download of “cube” structures to a desktop platform without the need for shared or cube server:

• MPLAP
• ROLAP

•    DOLAP                                    page 78

• HOLAP

ROLAP provides access to information via a relational database using:

•    ANSI standard SQL                                        page 78

• Proprietary file format
• Comma Separated Values
• All of the given option

——————– is usually deployed when expression can be used to group data together in such a way that access can be targeted to a small set of partitions:

• Expression elimination

•    Expression partitioning                                 page 67

• •         Expression indexing
• None of the given option

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

• Distributed Systems Architecture

•    Legacy System Architecture                                 page 14

• Online System Architecture
• Intranet System Architecture

The data has to be checked , cleaned and transformed into a ————— format to allow easy and fast access

•    Unified                                                                 page 20

• Predicated
• Qualified
• Proactive

Suppose in a system A, the values of “PhoneNo” attribute were stored in “countrycode-phone-extension” format, however after transformation into data warehouse the separate columns were used for “countrycode”,”phone” and “extension”. The above scenario is an example of :

• One-to-one scalar transformation

•    One-to-many element transformation         page 144+conceptual

• Many-to-one element transformation
• Many-to-many element transformation

In decision support system ease of use in achieved by:

• Normalization

•    Denormalization                     page no 49

• Drill up
• Drill down

Which of the following is one of the methods to simplify an ER model?

• Normalization

•    Denormalization                           page no 103

• HOLAP
• Hybrid schema

 In ETL process data transformation includes —————-

•    Data cleansing                                        page 129

• Data aggregation
• Behavior checking
• Pattern recognition

Non-uniform use of abbreviations, units, and values refers to:

•    Syntactically dirty data                       page 160

• Semantically dirty data
• Coverage anomaly
• Extraction issue

Suppose the size of the attribute “Computerized National Card (CNIC) no. is changed in NADRA database. This transformation refers to:

•    Format revision                                           page 153

• Field splitting
• Field decoding
• Calculation of derived value

The divide and conquer cube partitioning approach helps alleviate the ———— limitations of MOLAP implementation:

• Flexibility
• Maintainability
• Security

•    Scalability                                                 page 85

identify the TRUE statement:

• DM is inherently dimensional in nature
• DM comprises of a single central fact table
• DM comprises of a set of dimensional tables

•    All of the given option                               Page 103

————- can be used when some columns are rarely accessed rather than other columns or when the table has wide rows or header or both:

• Horizontal splitting
• Pre-joining

•    Vertical splitting                               page 56

• Derived attributes

Which of the following is an example of derived attributes?

•    Age                                                                    page 61

• Size
• Color
• Length

The online high performance transaction processing was evolved in ————–:

• 1980

•    1975                                                   page 12

• 1977
• 1965

Cube is a logical entity containing values of a certain aggregation level at an intersection of a combination of ——————– :

• Facts

•    Dimension                                            page 88

• Summary tables
• Primary and foreign key

Which of the following is TRUE regarding Entity relationship modeling?

• It does not really model business, but models the micro relationships among data elements.
• ER modeling does not have “business rules,” it has “data rules
• ER modeling helps retrieval of individual records having certain critical identifiers.

•    All of the given option                          page 102

——Facilitates a mobile computing paradiagramn:

• HOLAP
  • DOLAP               page78
• ROLAP
• MOLAP

The main reason(s )for the increase in cube size may be:

• Increase in the number of dimensions
• Increase in the cardinality of the dimensions
• Increase in the amount of detail data

•    All of the given options page 87

Suppose the amount of data recorded in an organization is doubled in year. This increase in ——

• Linear
• Quadratic

•    Exponential                            page 15

• Logarithmic

The data in the data warehouse is ———– :

• Volatile

•    Non-volatile                         page 69

• Static
• Non-structured

————— models the macro relationships among data elements with an overall deterministic strategy:

•    Dimensional model              page102

• Entity relationship model
• Object oriented model
• Structured model

—————– technique requires a separate column to specify the time and date when the last modification was occurred:

• Checkmarks

•    Timestamps           page 150

• Just-in-Time
• Real Time extraction

Which of the de-normalization technique squeezes master table into detail?

•    Pre-joining                     page 58

• Horizontal splitting
• Vertical splitting
• Adding redundant column

De-normalization can help:

• Minimize joins
• Minimize foreign keys
• Resolve aggregates

•    All of the given options page 51

The domain of the “gender” field in some database may be (‘F’,’M’) or as (“Female”, “Male”) or even as (1, 0). This is:

• Primary key problem

•    Non primary key problem        page 163

• Normalization problem
• All of the given option

Increasing level of normalization —————-	number of tables:
	•  Increases	page 51

• Decreases
• Does not effect
• None of the given option

Which of the following is not a Data Quality Validation Technique:

• Referential integrity
• Using Data Quality Rules
• Data Histograming

•    Indexes                    page 189

This technique can be used when column from one table is frequently accessed in a large scale join in conjunction with a column from another table:

• Horizontal splitting
• Pre-joining

•    Adding redundant column page 58

• Derived attributes

Data cleansing requires involvement of domain expert because:

• Domain expert has deep knowledge of data aggregation
• Change Data captures requires involvement of domain expert

•    Domain knowledge is required to correct anomalies     page 158

• Domain expert has deep knowledge of data summarization

Relational databases allow you to navigate the data in ————- that is appropriate using the primary , foreign key structure with in the data model:

• Only One Direction

•    Any Direction       page 19

• Two Direction
• None of these

History is excellent predicator of the ————:

• Past
• Present

•    Future                   page 15

• History

De- normalization is the process of selectively transforming normalized relations into un-normalized physical record specifications, with the aim to:

• Well structure the data
• Well model the data

•    Reduce query processing time page 50

• None of the given option

—————– gives total view of an organization:

• OLAP
• OLTP

•   Data Warehouse     page 16

• Database

Suppose in system A, the possible values of “Gender” attribute were “Male”& “Female”, however in data warehouse ,the values stored were “M” for male and “F” for female. This above scenario is an example of :

•   One-to-one scalar transformation      page 144

• One-to-many element transformation
• Many-to-one element transformation
• Many-to-many element transformation

Enrichment is one of the basic tasks in data —————- :

• Extraction

•    Transformation page 138

• Loading
• Summarization

Which of the following is not a technique of De-normalization?

• Pre-joining
• Splitting tables
• Adding redundant columns

•    ER modeling                  page 52

Which of the following is an example of Additive Facts?

•    Sales Amount              page 119

• Average
• Discount
• Ratios

Robotic libraries are needed for ————————-:

• Cubes
• Data marts

•    Data warehouse                page 131

• Aggregates

Normally ROLAP is implemented using —————-

•    Star schema        page 87

• Hybrid schema
• Pre-defined aggregate
• All of the given options

The relation R will be in 2^nd Normal Form if

• It is in 1NF and each cell contains single value

•    It is in 1NF and each non key attribute is dependent upon entire primary key     page 44

• It is in 1NF and non key attribute is dependent upon a single column of composite primary key
• It is in 1NF and Primary key is composite

In ———–	ested loop join of quadratic time complexity does not hurt the performance
	•	Typical OLTP environments	page 22

• Data warehouse
• DSS
• OLAP

In Extract, Load, Transform(ELT) process, data transformation —————:

•    Takes place on the data warehouse server  page 147

• Takes place on a separate transformation server
• Depends on the nature of the source database
• Does not take place

Node of a B-Tree is stored in memory block and traversing a B-Tree involves ————— page faults:

• O(n log n)

•    O(log n)                     page 22

• O(n)
• O(n2)

As dimensions get less detailed (e.g. , year vs. day) cubes get ——————–

•    Smaller                    page 84

• Larger
• Partitioned
• Merged

Which of the following is not a technique of “ Changed Data Capture” in currently used Modren

Source System?

• Timestamps
• Partitioning
• Triggers

•    Dimensional Modeling page 150

The trade-offs of de-normalization is/are:

• Storage
• Performance
• Ease-of-use

•    All of the given options    page 62

If actual data structure does not conform to documented formats then it is called:

•    Syntactically dirty data page 160

• Semantically dirty data
• Coverage anomaly
• Extraction issue

“Header size is reduced, allowing more rows per back , thus reducing I/O” .The above statement is TRUE with respect to:

•    Vertical splitting             page 56

• Horizontal splitting
• Adding redundant column

• None of the given options
• Question: Break a teble into Multiple Tables based upon Comomn column values
• Horizental Spliting
• Vertical splitting
• Adding redundant column
• None of the given option

Which of the following is NOT an example of derived attribute?

• Age
• CGPA
• Area of rectangle

•    Height        (Conceptual)

Which of the following is NOT an example of derived attribute?

• Age
• CGPA
• Annual Salary

If a table is expected to have six columns but some or all of the records do not have six columns then it is example of:

•    Syntactically dirty data       page 160

• Semantically dirty data
• Coverage anomaly
• Extraction issue

MDX by Microsoft is an example of ————————:

• HOLAP
• DOLAP
• ROLAP

•    None of the given options        page 79

The growth of master files and magnetic tapes exploded around the mid- —————

• 1950s

•    1960s               page 12

• 1970s
• 1980s

If one or more records in a relational table do not satisfy one or more integrity constraint , then the data:

• Is syntactically dirty

•    Is semantically dirty        page 160

• Has Coverage anomaly
• Has extraction issue

OLAP is:

•    Analytical processing   page 69

• Transaction processing
• Additive processing
• Active processing

One of the possible issues faced by web scrapping is that:

•    Web pages may contain junk data   page 141

• Web pages do not contain multiple facts
• Web pages do not contain multiple dimensions
• Web pages does not support transformation

Which of the following is\are example of dimension:

•    Product      page 79

• Region
• Data
• None of the given

An OLTP system is always good at ————————:

•    Evolving data  page 122

• Keeping static data
• Tracking past data
• Maintaining historic data

In case of multiple sources for the same data element , we need to prioritize the source systems per element based, the process is called:

•    Ranking      page 143

• Prioritization
• Element selection
• Measurement selection

One feature of Change Data Capture (CDC) is that:

• It pre-calculates changed aggregates
• It loads the transformed data in real time
• It only processes the data has been changed

•    It can automate the transformation of extracted data  page 149

In —————— SQL generation in vastly simplified for front-end tools when the data is highly structure:

• MOLAP

•    Star Schema                       page 107

• Hybrid schema
• Object oriented schema

Dirty data means:

• Data cannot be aggregated
• Data contains non-additive facts
• Data does not fulfill dimensional modeling rules

•    Data does not conform to proper domain definitions   page 158

In Context of Change Data Capture (CDC) sometimes a ————- object can be used to store recently modified data:

• Buffer table

•    Change table                 page 149

• Checkmark table
• Change control table

“Sometimes during data collection complete entities are missed”. This statement is an example of :

•    Missing tuple             page 161

• Missing attribute
• Missing aggregates
• Semantically dirty data

Table collapsing technique is applied in case of:

•    One-by-one relation or many-to –many relation page 52

• One-to-many relation
• Many-to-many relation
• None of the given option

Which of the following is an example of dimension?

• Product
• Region
• Date

•    All of the given option page 78

Data warehouse stores ——————-:

• Operational data

•    Historical data     page 24

• Meta data
• Log files data

The business process covered by ER diagrams:

•    Do not co-exist in time and space     page 109

• Co-exist in time and space
• Do not physically exist in real time context
• None of the given options

The main goal of normalization is to eliminate ———–:

•    Data redundancy  page 41

• Data sharing
• Data security
• Data consistency

Serious —- involves decomposing and resembling the data:

•    Data cleansing  page 168

• Data transformation
• Data loading
• Data extraction

In the data warehouse environment the data is ————

•    Subject- oriented           page 69

• Time- oriented
• Both subject and time oriented
• Neither time-oriented nor subject- oriented

For large record spaces and large number of records , the run time of the clustering algorithms:

•    Prohibitive             page 164

• Static
  • •         Exponential
• Numerical

————- can result in costly errors, such as , False frequency distributions and incorrect aggregates due to double counting:

•    Data duplication      page 165

• Data reduction
• Data anomaly
• Data transformation

The degree to which values are present in the attributes that require them is known as –

———————:

•    Completeness         page 185

• Uniqueness
• Accessibility
• Consistency

Time complexity of Key Creation process in basic Sorted Neighborhood (BSN) Method is

———————-:

• O(n log n)
• O(log n)

•   O(n)             page 171

• O(2n)

Which of the following is an example of slowly changing dimensions?

•    Inheritance      page 124

• Aggregation
• Association
• Asset disposal

The ———— operator proves useful in more complex metrices applicable to the dimensions and accessibility:

•    Max              page 188

• Min
• Max and Min
• None of the given

In OLAP , the typical write operation is ————- :

•    Bulk insertion    page 75

• Single insertion
• Sequential insertion
• No insertion

The issue(s) of “ Adding redundant column” includes(s):

• Increase in table size
• Maintenance
• Loss of information

•    All of the given option page 65

————– is applicable in Profitability analysis:

• OLTP

•    Data warehouse         page 36,37

• Information System(IS)
• Management Information System(MIS)

The hardware (CPU) utilization in data warehouse environment is full or ———– :

• Fixed
• Partial

•    Not at all          page 24

• Slow

Time variant is a characteristics of data warehouse which means:

•    Data loaded in data warehouse will be time stamped   page 20

• Data can be loaded in data warehouse anytime
• Data can be loaded in data warehouse only at a particular time
• Data cannot be loaded in data warehouse with respect to time

In which class of aggregates AVERAGE function can be placed:

•    Algebraic           page 120

• Distributed
• Associative
• Holistic

Considered the following Employee table and identify the column which causes that the table is not in first normal form(1NF): (Emp_ID, Emp_Name ,Emp_skills, Emp_Designation)

• Emp_ID
• Emp_Name

•   Emp_skills           page 43(conceptual)

• Emp_Designation

The application of data and information leads to ————-

• Intelligence
• Experience

•    Knowledge      page 11

• Power

————— segregate data into separate partitions so that queries do not need to

examine all data in a table when WHERE clause filters specify only a subset of the partitions.

• Pre-joining technique
• Collapsing table technique

•    Horizontal splitting technique page 56

• Vertical splitting technique

————-should not be present in a relation, so that it would be in second normal form (2NF).

•    Partial dependency         page 44 (conceptual)

• Full functional dependency
• Multivalued dependency
• Transitive dependency

Records referring to the same entity are represented in different formulas in the different data sets or are represented erroneously. Thus duplicate records will appear in the merged database. This problem is known as————.

•    Merge/purge problem           page 168

• Duplication problem
• Redundant duplication problem
• Redundant problem

The data perspective in OLTP system is operational, while that in data warehouse

is:

• Fully normalized
• Fully de-normalized
• Fully summarized

•    Historical and detailed           page 30

Simple scalar transformation is a————–mapping from one set of values to another set of values using straightforward rules.

•    One-to-one       page 144

• One-to-many
• Many-to-many
• Many-to-one

—————can be created in operational systems to keep tracks of recently updated records.

•    Triggers                   page 150

• Timestamps
• Partitioning
• ELT

Development of data warehouse is hard because data sources are usually——–

• Structured and homogeneous

•    Unstructured and heterogeneous     page 31

• Structured and heterogeneous
• Unstructured and homogeneous

In a decision support environment, the decision maker is interested in ————-.

• Only limited organizational data

•    Big picture of organizational data    page 21

• Only sale related data
• Only customer related data

Information can answer question like “what”, “who” and “when” while knowledge can answer question like—————-.

• Why
• Where
• Which

•    How                           page 11

OLTP implementations are fully————-.

•    Normalized                  page 69

• Denormalized
• Predictive

•    Additive

Which logical data extraction has significant performance impacts on the data warehouse server?

•    Incremental Extraction      page 133

• Online Extraction
• Offline Extraction
• Legacy Vs OLTP

Consider the following Student table and identify the column which causes that the table is not in first normal form(1NF).

Student(Std_ID, Std_Name ,Std_CGPA ,Std_Hobbies)

• •         Std_ID
• Std_Name
• Std_CGPA

•    Std_Hobbies       page 43(Conceptual)

Analytical processing uses —————

•    Multi-level aggregates     page 74

• Record level aggregates
• Table level aggregates
• All of the given options

Which is not a class of anomalies in following?

•    Dirty anomalies  page 160

• Syntactically dirty data
• Semantically dirty data
• Coverage anomalies

————- is a system of activities that assures conformance of product to pre-established requirements.

•    Quality assurance       page 183

• Quality improvement
• Quality Maintenance
• Quality Establishment

Two interesting examples of quality dimensions that can make use of min operator are ——

•    Believability and appropriate amount of data    page 188

• Believability and consistency
• Believability and Redundancy
• Reliability and appropriate amount of data

————– in database or data warehouse has no actual value; it only has potential

value.

•    Data            page 181

• Entity
• Flat tables
• Data marts

In OLTP environment the selectivity is ———— and ———- in data warehouse environment.

•    High, Low       page 22

• Low, High
• High, Fixed
• Fixed, Low

Which is not a/an characteristics of data quality?

•    Reliability           page 186

• Uniqueness
• Accessibility
• Consistency

If a product meets formally defined “requirement specifications”, yet fails to be a quality product from the customer’s perspective , this means the requirements were ———–.

•    Defective         page 180

• Unclear
• Unrefined
• Undefined

The relation R will be in 3^rd Normal Form if:

• It is in 2NF each cell contains single value
• It is in 2NF and every non-key column is non-key transitively dependent upon its primary

key.           Page 46

• It is in 1NF and each non key attribute is dependent upon a single column of composite primary key.
• It is in 2NF and each non key attribute is dependent upon other non-key attribute.

Decision support system queries deal with number of columns ————

• Having numeric values
• In a single table
• In a single view

•    Spanning across multiple tables       page 21

Normalization is used to reduce:

•    Reduces redundancy    page 41

• Increases redundancy
• Reduces joins

•    Reduces tables

The end user of data ware house are—————.

• Programmers
• Database developers
• Data entry operator

•    Business executives        page 18 + 19

Which one are the characteristics of data warehouse queries?

• Use primary key
• High selectivity

•    Use multiple tables   page 30

• Very low performance

Assume a company with a multi- million row customer table i.e. n rows. Checking for Referential Integrity (RI) using a naive approach would take —————— time.

•    O(n)            page 160

• O(1)
• O(log n)
• None of the given

Web scrapping is a process of applying ————- techniques to the web

•   Screen scrapping   page 146

• Data scrapping
• Text scrapping
• Meta scrapping

Which is not an issue of ROLAP in the following?

•    Standard hierarchy of dimensions  page 92

• Non-standard conventions
• Maintenance
• Aggregation

One of the fundamental purpose of de-normalization is to ——————— a number of physical tables which ultimately reduce the number of joins to answer a query.

• Delete

•    Reduce  page 50

• Increase
• Decrease

———– is not the characteristic of data warehouse.

• Time variant
• Subject-oriented
• Integrated

•    Volatile       page 69

Which is not a/an step of data cleansing procedure?

•    Aggregation              page 168

• Elementizing
• Standardizing
• Verifying

Instance matching between different sources is then achieved by a standard ————-

on identifying attribute(s), if you are very, very, very lucky.

•    Equi-join      page 169

• Inner join
• Outer join
• Fuller join

Ad-hoc access of data warehouse means:

• That have predefined database access pattern

•   That does not have predefined database access pattern        page 18

• That could be accessed by any user
• That could not be accessed by any user

In OLTP environment, the size of tables is relatively——————-

• Large
• Fixed
• Moderate

•   Small          page 22

————- is a/an measure of how current or up to date the data is:

•    Timeliness        page 185

• Completeness
• Accessibility
• Consistency

The process of converting entity relationship model in to dimensional model of ———-

steps:

• Two
• Three

•    Four           page 109

• Five

A ————- Is defined by a group of records that have similar characteristics (“behavior”) for p% of the fields in the data set, where p is a user- defined value(usually above 90).

•    Pattern        page 164

• Cluster
• Entity
• Attribute

—————— is known as state of being only one of its kind or being without an equal or

parallel.

• Completeness

•    Uniqueness             page 185

• Accessibility
• Consistency

Which of the following is not an example of fact?

•    Account no     page 74

• Sales quantity
• Per unit sales amount
• Sales amount

——————is the degree to which data is accurately reflects the real world object that the data represents.

•    Intrinsic data quality   page 181

• Realistic data quality
• Strong data quality
• Weak data quality

Which one among the following data warehouse stores data containing long period?

• Telecommunication data warehouse
• Financial data warehouse
• Human resource data warehouse

•   Insurance data warehouse  page 36

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

Data Warehouse is about taking / collecting data from different ________ sources.

• Harmonized
  • Identical
  • Homogeneous                         NOT CONFIRM
• Heterogeneous

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

It is observed that every year the amount of data recorded in an organization

• Doubles
  • Triples
• Quartiles

The users of data warehouse are knowledge workers in other words they are _________ in the organization.

• DWH Analyst
• Decision maker
  • Database Administrator
  • Manager

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

• O (n lg n)
• O (log n) { O(log n) it’s the real answer}
  • O (n)
• O (n2)

In _________ system, the contents change with time.

• OLTP
  • ATM
  • DSS
  • OLAP

The growth of master files and magnetic tapes exploded around the mid- _______.

• 1950s.
  • 1960s.
  • 1970s.
• 1980s.

Relational databases allow you to navigate the data in ____________ that is appropriate using the primary, foreign key structure within the data model

• Only One Direction
  • Any Direction
  • Two Direction
  • None of these

Naturally Evolving architecture occurred when an organization had a _______ approach to handling the whole process of hardware and software architecture.

• Relaxed
  • Good
• Not Relaxed
  • None

________ gives total view of an organization

• OLAP
  • OLTP
• Data Warehouse
  • Database

Suppose the amount of data recorded in an organization is doubled every year. This increase is

__________ .

• Linear
  • Quadratic
• Exponential
  • Logarithmic

Which people criticized Dimensional Modeling (DM) as being a data mart oriented approach?

Those that consider ER model as Data marts

Which of the following is not a CUBE operation?

ANSI SQL

If actual data structure does not confirm to documented formats then it is called:

Semantically dirty data

This technique can be used when a column from one table is frequently accessed in a large scale join in conjunction with

Adding redundant column

“Header size is reduced, allowing more rows per block, thus reducing I/O”. The above statement is TRUE with respect to:

Vertical splitting

Gives total view of an organization.

Data Warehouse

ROLAP provides access to information via a relational database using

ANSI standard SQL

The main reason(s) for the increase in cube size may be

All of the given

If each cell of Relation R contains a single value (no repeating values) then it is confirmed that

Relation R is in 3^rd Normal Form but in 2^nd Normal Form

In Extract, Load, Transformation (ELT) process, you don’t need to purchase extra devices to achieve parallelism because.

You already have parallel data warehouse servers

Which of the following is not a technique of “Changed Data Capture” in currently used Modern Source Systems?

Dimensional Modeling

Breaks a table into multiple tables based upon common column values

Horizontal splitting

Which of the following is NOT an example of derived attribute?

Height

Grain of a fact table means

The meaning of one fact table row

Which of the following is NOT an example of derived attribute?

Email Address

Which of the following is an example of Additive Facts?

Average

If a table is expected to have six columns but some or all of the records do not have six columns then it is example of:

Syntactically dirty data

Involves splitting a table by columns so that a group of columns is placed into the new table and the remaining

Vertical splitting

Normalization.

Reduces redundancy

De-normalization affects:

Database size and query performance

MDX by Microsoft is an example of                      .

None of the given options

The divide and conquer cube partitioning approach helps alleviate the          limitations of MOLAP implementation.

Scalability

Non uniform use of abbreviations, units and values refers to:

Syntactically dirty data

Allows download of “cube” structures to a desktop platform without the need for shared relational or cube server.

DOLAP

Node of a B-Tree is stored in memory block and traversing a B-Tree involves            page faults.

O (lg n)

The growth of master files and magnetic tapes exploded around the mid  

1960s.