Data Warehousing Test
Changing
the view of the data to a higher level of aggregation is known as:
a. Implosion
b. Drill
down
c. Drill
up
d. Synchronisation
e. Summarisation
Given
the following steps between raw data and extracted knowledge, arrange them in
the correct order:
1 Data
mining
2
Transformation
3
Selection
4
Pre-processing
5
Interpretation and Evaluation
a. 3,4,2,1,5
b. 4,3,2,1,5
c. 4,2,1,3,5
d. 4,1,2,3,5
e. 3,4,1,2,5
Metadatadoes not include:
a. The
actual data
b. A
description of tables and fields in the warehouse, including data types and the
range of acceptable values
c. A
similar description of tables and fields in the source databases, with a
mapping of fields from the source to the warehouse
d. A
description of how the data has been transformed, including formulae,
formatting, currency conversion, and time aggregation
e. Information
that is needed to support and manage the operation of the data warehouse
Which
technique of Data Mining involves developing mathematical structures with the
ability to learn?
a. Clustering
and Segmentation
b. Neural
Networks
c. Fuzzy
Logic
d. Linear
Regression Analysis
e. Rule
based Analysis
In a
star schema, a table which contains data about one of the dimensions is called
a:
a. Fact
table
b. Meta
table
c. Data
Dictionary
d. Pivot
table
e. Dimension
table
a. The
process of organising data in accordance with the rules of a relational
database
b. The
process of cleansing the data
c. The
process of integrating the data into the datawarehouse from legacy systems
d. The
process of compressing the data
e. The
process of eliminating invalid data before it is introduced into the data
warehouse
A
means of extending the data accessible to the end user beyond that which is
stored in the OLAP server is know as :
a. Consolidation
b. Multi
Dimensional Analysis
c. Drill
Down
d. Navigation
e. Reach
through
Which
of the following rules would be considered the central core of OLAP?
a. Multidimensional
Conceptual View
b. Intuitive
Data Manipulation
c. Accessibility
d. Batch
Extraction vs Interpretative
e. Transparency
Which
is not a purpose of Data Mining?
a. Decision
Support
b. Prediction
c. Forecasting
d. Estimation
e. OLTP
Which
of the following type of data is most likely to be stored on some form of mass
storage ?
a. Metadata
b. Highly
summarised data
c. Lightly
summarised data
d. Current
detail data
e. Older
detail data
Under
OLAP terminology, slice and dice refers to:
a. The
user-initiated process of navigating by calling for page displays
interactively, through the specification of slices via rotations and drill
down/up
b. Restricting
the view of database objects to a specified subset
c. A
means of extending the data accessible to the end user beyond that which is
stored in the OLAP server
d. Computing
all of the data relationships for one or more dimensions
e. Applying
calculations to input data at the time the request for that data is made
The
movement of data from one environment to another is known as:
a. Data
Migration
b. Normalization
c. Replication
d. Data
Mining
e. Data
Cleansing
The
term OLAP was coined by:
a. Date
b. Codd
c. IBM
d. Oracle
e. Microsoft
A
data warehouse includes data from various sources including legacy systems.
Legacy systems implies:
a. Systems
that have been developed at different times by different people for a variety
of purposes
b. Systems
which are no longer useful
c. Systems
whose data is outdated
d. Systems
whose technology is outdated
e. Systems
whose data is corrupt
A
Star Schema is a database design that consists of:
a. A
fact table
b. Dimension
tables
c. Pivot
tables
d. A
fact and pivot tables
e. A
fact table and one or more dimension tables
Which
of the following would be the only similarity between a datawarehouse and OLTP
system?
a. Purpose
b. Structure
of data
c. Type
of data
d. Condition
of data
e. Data
model
Which
Data Mining function/technique is used to analyse a collection of records over
a period of time?
a. Classification
b. Associations
c. Sequential/Temporal
patterns
d. Clustering
e. Segmentation
A
data warehouse is a "subject-oriented, integrated, time-variant,
non-volatile collection of data in support of management's decision-making
process". The data within the warehouse is integrated in such a way that:
a. Users
from all departments help to create the database
b. It
contains the data of the enterprise in its entirety
c. The
final product is a fusion of various legacy system information into a cohesive
set of information
d. Every
user has access to the data in the warehouse
HOLAP stands for:
a. Hierarchical
On-line Analytical Processing
b. Hybrid
On-line Analytical Processing
c. Horizontal
On-line Analytical Processing
d. Hyper
On-line Analytical Processing
e. HyperCube
On-line Analytical Processing
Changing
the view of the data to a greater level of detail is known as:
a. Explosion
b. Drill
down
c. Drill
up
d. Exploration
e. Aggregation
In
which component of the enterprise is the data re-organised for analysis and
information extracted from the data?
a. The
Data Warehouse
b. The
Data Mart
c. The
Data Mine
d. The
operational RDBMS
e. Metadata
Which
of the following is not true regarding an OLTP system?
a. OLTP
is generally regarded as unsuitable for data warehousing
b. OLTP
systems can be repositories of facts and historical data for business analysis
c. The
purpose of an OLTP system is to run day-to-day operations
d. The
Data Model of an OLTP system is normalised
e. OLTP
offers large amounts of raw data
Dataquality management refers to the fact that:
a. Ad-hoc
analysis must not be slowed or inhibited by the performance of the data
warehouse RDBMS
b. The
warehouse must ensure local consistency, global consistency, and referential
integrity
c. The
RDBMS server must support hundreds, even thousands, of concurrent users while
maintaining acceptable query performance
d. The
server must include tools that co-ordinate the movement of subsets of data
between warehouses
e. The
RDBMS must provide a complete set of analytic operations including core
sequential and statistical operations
A
structure that stores multi-dimensional information, having one cell for each
possible combination of dimensions is known as:
a. Table
b. Section
c. Partition
d. Cube
e. Repository
Which
of the following stage is concerned with the extraction of patterns from the
data?
a. Selection
b. Pre-processing
c. Transformation
d. Data
Mining
e. Interpretation
and Evaluation
Which
of the following features are required by OLAP applications?
a. Multidimensional
views of data
b. Calculation-intensive
capabilities
c. Time
intelligence
d. All
of the above
The
main objects used by OLAP programs are:
a. Multidimensional
cubes
b. Metadata
c. RDBMS
tables
d. Fact
tables
e. Pivot
tables
A
data warehouse is a "subject-oriented, integrated, time-variant,
non-volatile collection of data in support of management's decision-making
process". The term non-volatile means that:
a. The
data is refreshed often
b. The
data is backed up often
c. The
data is deleted often
d. The
data is rarely changed
e. The
data is of low volume
Normalization applied to the dimension tables of a star schema is known as:
a. Snowflaking
b. Synchronization
c. Slicing
and Dicing
d. Replication
e. Data
transformation
SQL stands for:
a. Structured
Query Language
b. Systematic
Query Language
c. Structured
Query Logic
d. Structured
Queuing Logic
e. Standard
Query Logic
Which
of the following are the modes of OLAP?
a. MOLAP
b. ROLAP
c. KOLAP
DataMining is also known as
a. Data
Extraction
b. Data
Cleansing
c. Data
Archiving
d. Knowledge
Discovery in Databases (KDD)
e. Data
Preservation
A
multi-dimensional data set is sparse if:
a. The
data to be analysed is less in volume
b. If a
relatively high percentage of the possible combinations (intersections) of the
members from the data set's dimensions contain missing data
c. If a
relatively high percentage of the possible combinations (intersections) of the
members from the data set's dimensions contain invalid data
d. If a
relatively high percentage of the possible combinations (intersections) of the
members from the data set's dimensions contain valid data
e. If a
relatively high percentage of the possible combinations (intersections) of the
members from the data set's dimensions contain outdated data
Which
of the following techniques can be used to improve query performance?
a. Denormalization
b. Partitioning
c. Summarization
d. Denormalization
and Partitioning
e. Denormalization,
Partitioning and Summarization
Which
of the following is not associated with data warehousing?
a. Transaction
processing
b. Information
retrieval and analysis
c. Multi-dimensional
data model
d. Query
processing
e. Transformed
and summarised data
The
main objective of Data Mining is:
a. The
safe storage of data
b. Elimination
of errors from the data
c. Deleting
data that is no longer important to the organization
d. The
extraction of implicit, previously unknown, and potentially useful information
from data
e. To
help in the generation of reports for the management
Granularity refers to the:
a. Validity
of the data stored in a data warehouse
b. The
level of detail of the facts stored in a data warehouse
c. The
timeliness of the data stored in a data warehouse
d. The
redundancy of the data stored in a data warehouse
e. Compactness
of the data stored in a data warehouse
The
applications of Data Mining would not include:
a. Discovering
buying-patterns for cross selling
b. Financial
market prediction
c. Discovering
errors made during data entry
d. Discovering
which customer is most profitable
e. Credit
assessment
OLAP
queries can be characterised as on-line transactions that do not:
a. Access
small amounts of data
b. Analyse
the relationships between many types of business elements e.g. sales, products,
regions, and channels
c. Compare
aggregated data over hierarchical time periods e.g. monthly, quarterly, yearly
d. Present
data in different perspectives e.g. sales by region vs. sales by channels by
product within each region
e. Respond
quickly to user requests, so that users can pursue an analytical thought
process without being stymied by the system
A
multidimensional cube records a set of data derived from:
a. Fact
tables
b. Pivot
tables
c. Dimensions
d. Fact
tables and Dimensions
e. Fact
tables and Pivot tables